Mail Code: 94305-2150
Phone: (650) 498-8720
Email: symsys-afs@lists.stanford.edu
Web Site: http://symsys.stanford.edu
Courses offered by the Symbolic Systems Program are listed under the subject code SYMSYS on the Stanford Bulletin's ExploreCourses web site.
The observation that both human beings and computers can manipulate symbols lies at the heart of Symbolic Systems, an interdisciplinary program focusing on the relationship between natural and artificial systems that represent, process, and act on information. Computer programs, natural languages, the human mind, and the Internet embody concepts whose study forms the core of the Symbolic Systems curriculum, such as computation, representation, communication, and intelligence. A body of knowledge and theory has developed around these notions, from disciplines such as philosophy, computer science, linguistics, psychology, statistics, neurobiology, and communication. Since the invention of computers, researchers have been working across these disciplines to study questions such as: in what ways are computers and computer languages like human beings and their languages; how can the interaction between people and computers be made easier and more beneficial?
The core requirements of the Symbolic Systems Program (SSP) include courses in symbolic logic, the philosophy of mind, formal linguistics, cognitive psychology, programming, the mathematics of computation, statistical theory, artificial intelligence, and interdisciplinary approaches to cognitive science. These courses prepare students with the vocabulary, theoretical background, and technical skills needed for study and research at the advanced undergraduate and graduate levels. Most of the courses in SSP are drawn from affiliated departments. Courses designed specifically for the program are aimed at integrating and supplementing topics covered by the department-based offerings. The curriculum includes humanistic approaches to questions about language and intelligence, as well as training in science and engineering.
SSP offers B.S. and M.S. degree programs. Both programs require students to master a common core of required courses and to choose an area of specialization.
Mission of the Undergraduate Program in Symbolic Systems
The undergraduate program in Symbolic Systems is an interdisciplinary program focusing on the relationships between natural and artificial systems that use symbols to communicate and to represent information. The mission of the program is to prepare majors with the vocabulary, theoretical background, and technical skills necessary to research questions about language, information, and intelligence, both human and machine. The curriculum offers a combination of traditional humanistic approaches to these questions as well as a training and familiarity with contemporary developments in the science and technology of computation. Students in the major take courses in cognitive science, computer programming, logic and computational theory, probability, cognitive psychology, philosophy of mind, linguistics, and artificial intelligence. The program prepares students for a variety of careers in the private and public sectors, especially those involving the human-facing sides of information systems/technology, as well as for further study and research in the cognitive and/or information sciences.
Learning Outcomes (Undergraduate)
The program expects its undergraduate majors to be able to demonstrate the following learning outcomes. These learning outcomes are used in evaluating students and the Symbolic Systems Program. Students are expected to demonstrate:
1. ability to apply formal, philosophical, and/or computational analysis to experimental designs and data and vice versa.
2. ability to understand multiple formal, philosophical, and/or computational frameworks and how they are related to each other.
3. ability to map real world problems or observed phenomena onto formal, philosophical and/or computational frameworks and vice versa.
Learning Outcomes (Graduate)
The purpose of the master's program is to further develop knowledge and skills in Symbolic Systems and to prepare students for a professional career or doctoral studies. This is achieved through completion of courses representing each of the core disciplines of Symbolic Systems as well as an individualized course program in support of the completion of a Master's thesis.
Bachelor of Science in Symbolic Systems
The program offers a Bachelor of Science in Symbolic Systems, as well as an Bachelor of Science with Honors in Symbolic Systems and a Minor in Symbolic Systems. A major in Symbolic Systems qualifies as a Science, Technology, Engineering, and Mathematics (STEM) major under the U.S. Department of Homeland Security's Designated Degree Programs list of STEM programs. Depending on the plan of study, Sym Sys students can be classified as studying Cognitive Science (2010 CIP Code 30.2501) and/or Informatics (2010 CIP Code 11.0104).
Students declaring the major prior to 2020-21 should consult previous Stanford Bulletins for degree requirements. Such students should consult the student services office if they want to change to the new requirements.
How to Declare the Major
To declare a major in Symbolic Systems, a student must:
- Be enrolled in or have completed SYMSYS 1 Minds and Machines
- Declare the major in Axess, and have the declaration approved by the program student services officer.
- Submit a preliminary Course Plan form for the major to a declaration interview with one of the Advising Fellows or with the Associate Director of the Program; see the calendar of Office Hours on the Symsys website for possible interview times.
Advising
Upon declaration approval, students are assigned to both the Program Director and Associate Director as major advisors. The student must also select and confirm a concentration advisor.
- Declared majors have until the Autumn Quarter of their junior year to select a concentration advisor. Juniors declaring the major must have a concentration advisor confirmed at the time of declaration.
- A hold is placed on Winter Quarter registration for juniors who do not have a concentration advisor by Autumn Quarter of their junior year. (See the COVID-19 Policies tab for a one-year extension to Winter Quarter for this requirement.)
- Any individual with an ongoing instructional appointment at Stanford (listed as such in Chapters 2, 6, or 9 of the Faculty Handbook) may serve as the concentration advisor. To confirm a concentration advisor after an eligible faculty member has agreed to fill this role, student must send an email message to symsys-sso@stanford.edu and the concentration advisor, including a statement of how the student plans to fulfill the capstone requirement of the major. Changes to capstone plans require the approval of the concentration advisor.
Degree Requirements
The Symbolic Systems major requires completion of:
- The core: a common set of foundations, breadth requirements, and experiential requirements that all students in the program must complete
- An approved concentration: depth in a particular specialization chosen by the student. See a list of Concentrations below.
Students must submit a course plan to the student services officer for Symbolic Systems at least two quarters prior to the planned graduation date, listing courses taken or that will be completed to fulfill the course requirements for the major.
Students must obtain approval for any courses not listed as approved for a major requirement.
All courses taken to fulfill a major requirement for Symbolic Systems must be passed for 3 units or more, with either a letter grade ('C-' or better for core courses, and a 'D-' or above for concentration courses) a no-option pass grade ('S' or its equivalent in the Graduate School of Business, Stanford Law School, or School of Medicine, or in an approved transfer credit course from another institution. A 'CR' cannot be used to fulfill a major requirement for Symbolic Systems), except as modified by the COVID-19 policies in effect during 2020-21. Students who have already completed a required course with a 'CR' grade may file a Replacement Petition to take a course in the same subject area at the same or a higher level in order to avoid having to retake the course.
Unless otherwise stated, each course that is counted for the major must be taken for 3 units or more. Taking a course for 3 units is sufficient unless the requirement specifically states otherwise.
Each course taken for the major may be counted toward at most one required course in either the Core or Concentration (not both), except in cases where double-counting is explicitly allowed.
Students in a dual degree program, students taking a minor, or students in coterminal program, may not double-count courses towards different degree programs or minors unless a course is an introductory skill requirement for both majors.
The program is open to requests to approving courses not listed as options to fulfill major requirements. Consult the student services office for details of this process.
Core
Core requirements are typically completed earlier than a student's concentration, but the only requirements that impose explicit restrictions on when a course can be completed during a student's undergraduate career are the gateway and capstone requirements.
Course Requirements
| Units | ||
|---|---|---|
| 1. Preparations | 4 | |
| These courses should be completed early in the major. | ||
| a. Gateway Course | ||
| SYMSYS 1 | Minds and Machines | 4 |
| b. Single Variable Calculus | 10 | |
| One of the following: | ||
| MATH 19, MATH 20, and MATH 21 (or MATH 21A): Calculus | ||
| 10 units of Advanced Placement Calculus credit | ||
| Placement by the Mathematics Placement Diagnostic into MATH 20 or MATH 21 and completion of the rest of the series, or into MATH 51 | ||
| c. Multivariate Systems | 3-6 | |
| One of the following: | ||
| CME 100 | Vector Calculus for Engineers | 5 |
| CME 100A | Vector Calculus for Engineers, ACE | 6 |
| MATH 51 | Linear Algebra, Multivariable Calculus, and Modern Applications | 5 |
| MATH 51A | Linear Algebra, Multivariable Calculus, and Modern Applications, ACE | 6 |
| MATH 61CM | Modern Mathematics: Continuous Methods | 5 |
| MATH 61DM | Modern Mathematics: Discrete Methods | 5 |
| d. Further Study in Multivariate Systems | 3-5 | |
| Optional, but recommended, and may be used as contingent electives in a concentration. One or more of the following courses, which may be needed as preparation for some Core options and other advanced courses in the major. | ||
| CME 102 | Ordinary Differential Equations for Engineers (and (optionally) CME 104) | 5 |
| CME 102A | Ordinary Differential Equations for Engineers, ACE (, ACE, and (optionally) CME 104A, ACE) | 6 |
| CME 104 | Linear Algebra and Partial Differential Equations for Engineers | 5 |
| ENGR 108 | Introduction to Matrix Methods (formerly CME 103) | 3-5 |
| MATH 52 | Integral Calculus of Several Variables | 5 |
| MATH 53 | Ordinary Differential Equations with Linear Algebra | 5 |
| MATH 62CM | Modern Mathematics: Continuous Methods | 5 |
| MATH 62DM | Modern Mathematics: Discrete Methods | 5 |
| MATH 63CM | Modern Mathematics: Continuous Methods | 5 |
| MATH 104 | Applied Matrix Theory | 3 |
| MATH 113 | Linear Algebra and Matrix Theory | 3 |
| 2. Breadth Requirements | 9-15 | |
| One three quarter sequence of training in each of four methodological areas, plus a Cross-Area Requirement. | ||
| a. Philosophical Analysis | ||
i. An introductory course in the Philosophy Department | ||
| One of the following: | ||
| Any course listed with a PHIL number (with the exception of PHIL 99/SYMSYS 1) | ||
| THINK 69 | Emotion | 4 |
ii. Writing in the Major (WIM) course | ||
| PHIL 80 | Mind, Matter, and Meaning | 5 |
iii. An advanced undergraduate Philosophy course that lists PHIL 80 as a prerequisite | ||
| One of the following: | ||
| PHIL 107B | Plato's Later Metaphysics and Epistemology | 4 |
| PHIL 167D | Philosophy of Neuroscience | 4 |
| PHIL 172 | History of Modern Moral Philosophy | 4 |
| PHIL 173B | Metaethics | 4 |
| PHIL 175 | Philosophy of Law | 4 |
| PHIL 180 | Metaphysics | 4 |
| PHIL 180A | Realism, Anti-Realism, Irrealism, Quasi-Realism | 4 |
| PHIL 181 | Philosophy of Language | 4 |
| PHIL 182 | Advanced Philosophy of Language | 4 |
| PHIL 182A | Naturalizing Representation | 4 |
| PHIL 182H | Truth | 4 |
| PHIL 184 | Topics in Epistemology | 4 |
| PHIL 186 | Philosophy of Mind | 4 |
| PHIL 187 | Philosophy of Action | 4 |
| PHIL 189G | Fine-Tuning Arguments for God's Existence | 4 |
| b. Formal Methods | ||
| Courses that focus on rigorous definitions, axioms, theorems, and proofs, and their use in developing mathematical theories and meta-theories. Each of the following: | ||
i. Formal Logic | ||
| One of the following: | ||
| CS 157 | Computational Logic | 3 |
| PHIL 150 | Mathematical Logic | 4 |
| PHIL 151 | Metalogic (Prerequisite: PHIL 150 or instructor permission) | 4 |
ii. Theory of Computation. One of the following: | ||
| CS 103 | Mathematical Foundations of Computing (Corequisite: CS 106B or X) | 3-5 |
| CS 154 | Introduction to the Theory of Computation (Prerequisite: CS 103 or significant proof-writing experience.) | 3-4 |
iii. Probability Theory and Statistics | ||
| A course that covers the theory of probability and is grounded in multivariable calculus. One of the following: | ||
| CME 106 | Introduction to Probability and Statistics for Engineers | 4 |
| CS 109 | Introduction to Probability for Computer Scientists | 3-5 |
| EE 178 | Probabilistic Systems Analysis | 3-4 |
| MATH 151 | Introduction to Probability Theory | 3 |
| MATH 63DM | Modern Mathematics: Discrete Methods | 5 |
| MS&E 120 | Introduction to Probability | 4 |
| MS&E 220 | Probabilistic Analysis | 3-4 |
| STATS 110 | Statistical Methods in Engineering and the Physical Sciences | 5 |
| STATS 116 | Theory of Probability | 4 |
| c. Computational Methods | ||
| Courses that focus on software design, data structures, algorithms, development, applications, evaluation, and simulation. Each of the following: | ||
i. Programming 1 | ||
| One of the following: | ||
| CS 106A | Programming Methodology | 3-5 |
| Equivalent preparation, as evidenced by successful completion of CS 106B or 106X | ||
ii. Programming II | ||
| One of the following: | ||
| CS 106B | Programming Abstractions | 3-5 |
| CS 106X | Programming Abstractions (Accelerated) | 3-5 |
iii. A post CS 106B course covering one or more broad computational methods with a programming component. | ||
| One of the following: | ||
| CS 107 | Computer Organization and Systems | 3-5 |
| CS 107E | Computer Systems from the Ground Up | 3-5 |
| CS 129 | Applied Machine Learning | 3-4 |
| CS 147 | Introduction to Human-Computer Interaction Design (Plus one of the following:) | 3-5 |
| CS 193A | Android Programming | 3 |
| CS 193C | Client-Side Internet Technologies | 3 |
| CS 193P | iOS Application Development | 3 |
| CS 193X | Web Programming Fundamentals | 3 |
| CS 194H | User Interface Design Project | 3-4 |
| CS 221 | Artificial Intelligence: Principles and Techniques | 3-4 |
| CS 229 | Machine Learning | 3-4 |
| d. Empirical Cognitive Science | ||
| Courses that focus on questions, hypotheses, models, predictions, and explanations that are derived from or testable in neural and behavioral data. Each of the following: | ||
i. Overview of psychology. | ||
| PSYCH 1 | Introduction to Psychology | 5 |
ii. An introductory area course in cognition, language, and neuroscience. | ||
| One of the following: | ||
| BIO 150 | Human Behavioral Biology | 5 |
| LINGUIST 145 | Introduction to Psycholinguistics | 4 |
| LINGUIST 150 | Language and Society | 3-4 |
| PSYCH 30 | Introduction to Perception | 4 |
| PSYCH 45 | Introduction to Learning and Memory | 3 |
| PSYCH 50 | Introduction to Cognitive Neuroscience | 4 |
| PSYCH 60 | Introduction to Developmental Psychology | 3 |
| PSYCH 70 | Self and Society: Introduction to Social Psychology | 4 |
| PSYCH 75 | Introduction to Cultural Psychology | 5 |
| PSYCH 141 | Cognitive Development | 3 |
| PSYCH 154 | Judgment and Decision-Making | 3 |
iii. Linguistic Theory | ||
| A course introducing a core area of theoretical inquiry in linguistics. One of the following: | ||
| LINGUIST 105 | Phonetics | 4 |
| LINGUIST 110 | Introduction to Phonology | 4 |
| LINGUIST 120 | Introduction to Syntax | 4 |
| LINGUIST 130A | Introduction to Semantics and Pragmatics | 4 |
| LINGUIST 130B | Introduction to Lexical Semantics | 3-4 |
| Additional approved undergraduate courses offered on a semi-regular basis: | ||
| LINGUIST 21N | Linguistic Diversity and Universals: The Principles of Language Structure | 3 |
| LINGUIST 30N | Linguistic Meaning and the Law | 3 |
| LINGUIST 121A | The Syntax of English | 4 |
| LINGUIST 121B | Crosslinguistic Syntax | 4 |
| LINGUIST 134A | The Structure of Discourse: Theory and Applications | 2-4 |
| LINGUIST 160 | Introduction to Language Change | 2-4 |
| Cross-Area Requirement | ||
| A non-introductory course, which has as a prerequisite at least one Core course (or equivalent), and which combines methods and subject matter from at least two Breadth areas in the Core. One of the following: | ||
i. Suggested courses for most students | ||
| Only one course must be chosen to fulfill the requirement - categories are for guidance only: | ||
| CS 147 | Introduction to Human-Computer Interaction Design | 3-5 |
| CS 229 | Machine Learning | 3-4 |
| LINGUIST 130A | Introduction to Semantics and Pragmatics | 4 |
| LINGUIST 180 | From Languages to Information | 3-4 |
| PHIL 152 | Computability and Logic | 4 |
| PHIL 154 | Modal Logic | 4 |
| PHIL 167D | Philosophy of Neuroscience | 4 |
| PHIL 181 | Philosophy of Language | 4 |
| PSYCH 204 | Computation and Cognition: The Probabilistic Approach | 3 |
| PSYCH 209 | Neural Network Models of Cognition | 4 |
ii. Any other course on the full list of courses approved for this requirement below. | ||
| 3. Experiential Requirements | ||
| Each of the following: | ||
| a. Advanced Small Seminar Requirement. | ||
| An approved course which (a) builds on the Core Preparations and Breadth Requirements, (b) is small -- 20 students or fewer, and (c) is an interactive, discussion-based seminar. May be double-counted for an applicable Concentration requirement, but not for a Core requirement. | ||
| b. Capstone | ||
| A two-course requirement consisting of the following components, chosen in consultation with and approved by a student's Concentration Advisor (3 or more units each): | ||
i. Practicum | ||
| A project or internship-accompanying course. One of the following: | ||
| SYMSYS 190 | Senior Honors Tutorial | 1-5 |
| An approved project course with a SYMSYS listing in the 195-series. Any of the following: | ||
| SYMSYS 195A | Design for Artificial Intelligence | 3-4 |
| SYMSYS 195B | Design for Behavior Change | 3-4 |
| SYMSYS 195D | Research in Digital Democracy | 3-4 |
| SYMSYS 195E | Experimental Methods | 3 |
| SYMSYS 195G | Introduction to Game Design | 3-4 |
| SYMSYS 195I | Image Systems Engineering | 1-3 |
| SYMSYS 195L | Methods in Psycholinguistics | 4 |
| SYMSYS 195N | Natural Language Processing with Deep Learning | 3-4 |
| SYMSYS 195S | Service Design | 3-4 |
| SYMSYS 195U | Natural Language Understanding | 3-4 |
| SYMSYS 195V | Data Visualization | 3-4 |
| Supervised Research | ||
Taken with a faculty member on an approved symbolic-systems related project, taken as SYMSYS 196: Independent Study, or a department-based directed research course. | ||
| SYMSYS 192: Symbolic Systems in Practice (must be taken in conjunction with an approved internship or service project) | ||
ii. Integrative Requirement | ||
| Either an additional research project course (e.g., the second course of an Honors Project) or a Concentration-Specific Integrative Course, which must be completed no earlier than the Junior Year. Units must be applied to a student's concentration. | ||
| One of the following (the first three bulleted options are the Standard Options available across all Concentrations): | ||
| SYMSYS 190 | Senior Honors Tutorial (continuation of the course taken for the Practicum requirement) | 1-5 |
| An approved project course with a SYMSYS listing in the 195-series | ||
(See list under "Practicum" above - may be either the second quarter of a 2-quarter course, or a one-quarter course) | ||
| Supervised research with a faculty member on an approved symbolic-systems related project, taken as SYMSYS 196 Independent Study, or a department-based directed research course (may be either the second quarter of a 2-quarter course or a one-quarter course) | ||
| An approved Concentration-Specific Integrative Course taken within a Concentration. | ||
| Total Units | 75-90 | |
Full List of Cross-Area Requirement Courses
| Units | ||
|---|---|---|
| Cross-Area Requirement | ||
| The full list of approved courses for the Cross-Area Requirement. | ||
| Only one course must be chosen to fulfill the requirement - categories are for guidance only: | ||
| Philosophical Analysis and Formal Methods | ||
| PHIL 152 | Computability and Logic | 4 |
| PHIL 154 | Modal Logic | 4 |
| PHIL 162 | Philosophy of Mathematics | 4 |
| PHIL 181 | Philosophy of Language | 4 |
| Philosophical Analysis and Computational Methods | ||
| CS 181 | Computers, Ethics, and Public Policy | 4 |
| CS 182 | Ethics, Public Policy, and Technological Change | 5 |
| PHIL 152 | Computability and Logic | 4 |
| PHIL 167D | Philosophy of Neuroscience | 4 |
| Philosophical Analysis and Empirical Cognitive Science | ||
| PHIL 167D | Philosophy of Neuroscience | 4 |
| PHIL 181 | Philosophy of Language | 4 |
| PHIL 186 | Philosophy of Mind | 4 |
| Formal Methods and Computational Methods | ||
| CS 151 | Logic Programming | 3 |
| CS 154 | Introduction to the Theory of Computation | 3-4 |
| CS 161 | Design and Analysis of Algorithms | 3-5 |
| CS 229 | Machine Learning | 3-4 |
| CS 238 | Decision Making under Uncertainty | 3-4 |
| LINGUIST 130A | Introduction to Semantics and Pragmatics | 4 |
| LINGUIST 180 | From Languages to Information | 3-4 |
| PHIL 152 | Computability and Logic | 4 |
| PHIL 154 | Modal Logic | 4 |
| PSYCH 204 | Computation and Cognition: The Probabilistic Approach | 3 |
| PSYCH 209 | Neural Network Models of Cognition | 4 |
| PSYCH 221 | Image Systems Engineering | 1-3 |
| PSYCH 242 | Theoretical Neuroscience | 3 |
| PHIL 249 | Evidence and Evolution | 3-5 |
| Formal Methods and Empirical Cognitive Science | ||
| PSYCH 253 | Advanced Statistical Modeling | 3 |
| CS 229 | Machine Learning | 3-4 |
| ECON 178 | Behavioral Economics | 5 |
| LINGUIST 130A | Introduction to Semantics and Pragmatics | 4 |
| LINGUIST 180 | From Languages to Information | 3-4 |
| PHIL 154 | Modal Logic | 4 |
| PHIL 181 | Philosophy of Language | 4 |
| PSYCH 204 | Computation and Cognition: The Probabilistic Approach | 3 |
| PSYCH 209 | Neural Network Models of Cognition | 4 |
| PSYCH 221 | Image Systems Engineering | 1-3 |
| PSYCH 242 | Theoretical Neuroscience | 3 |
| PSYCH 249 | Large-Scale Neural Network Modeling for Neuroscience | 1-3 |
| PSYCH 253 | Advanced Statistical Modeling | 3 |
| Computational Methods and Empirical Cognitive Science | ||
| CS 147 | Introduction to Human-Computer Interaction Design | 3-5 |
| CS 229 | Machine Learning | 3-4 |
| CS 448B | Data Visualization | 3-4 |
| LINGUIST 130A | Introduction to Semantics and Pragmatics | 4 |
| LINGUIST 180 | From Languages to Information | 3-4 |
| PHIL 167D | Philosophy of Neuroscience | 4 |
| PSYCH 164 | Brain decoding | 3 |
| PSYCH 204 | Computation and Cognition: The Probabilistic Approach | 3 |
| PSYCH 209 | Neural Network Models of Cognition | 4 |
| PSYCH 221 | Image Systems Engineering | 1-3 |
| PSYCH 204A | Human Neuroimaging Methods | 3 |
| PSYCH 242 | Theoretical Neuroscience | 3 |
| PSYCH 249 | Large-Scale Neural Network Modeling for Neuroscience | 1-3 |
| PSYCH 253 | Advanced Statistical Modeling | 3 |
Concentration Areas
Please note: the concentrations areas are being revised, and new ones being added.
Applied Logic
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Applied Logic. All courses must be taken for 3 units of more. | ||
| Metalogic | 3-5 | |
| Metalogic | ||
| Computability | 3-5 | |
| Select one of the following: | ||
| Introduction to the Theory of Computation | ||
| Computability and Logic | ||
| Computational Approaches to Logic | 3-5 | |
| Select one of the following: | ||
| Logic Programming | ||
| Computational Logic | ||
| Set Theory | 3-5 | |
| Set Theory | ||
| Integrative Requirement. Must be completed no earlier than the Junior Year. | 3-5 | |
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Logic Programming | ||
| The Practice of Theory Research | ||
| Computational Law | ||
| General Game Playing | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Programming Languages | ||
| Computational Complexity | ||
| Programming Language Foundations | ||
| Introduction to Semantics and Pragmatics | ||
| Advanced Semantics | ||
| Modal Logic | ||
| Philosophy of Mathematics | ||
| Formal Epistemology | ||
| Measurement Theory | ||
| Logic and Artificial Intelligence | ||
| Topics in Logic, Information and Agency | ||
| Computation and Cognition: The Probabilistic Approach | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Computational Complexity II | ||
| Advanced Topics in Semantics & Pragmatics | ||
| Proofs and Modern Mathematics | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Artificial Intelligence
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-21 must complete the following requirements to qualify for a Concentration in Artificial Intelligence. All courses must be taken for 3 units of more. | ||
| Students in this Concentration are urged to take CS 161, either for the Core Cross-Area Requirement, or as a Contingent Elective, and prior to taking CS 221. | ||
| Programming | 3-5 | |
| Select one of the following: | ||
| Computer Organization and Systems | ||
| Computer Systems from the Ground Up | ||
| Introduction | 3-5 | |
| Artificial Intelligence: Principles and Techniques | ||
| Artificial Intelligence Depth | 3-5 | |
| Two courses chosen from the "select" list of AI courses (category B of the MSCS AI Track): | ||
| Introduction to Robotics | ||
| Natural Language Processing with Deep Learning | ||
| Spoken Language Processing | ||
| Natural Language Understanding | ||
| Machine Learning with Graphs | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Convolutional Neural Networks for Visual Recognition | ||
| Reinforcement Learning | ||
| Decision Making under Uncertainty | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Language and Technology | ||
| Advanced Topics in Human Virtual Representation | ||
| Computer Vision: Foundations and Applications | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Machine Learning Theory | ||
| Data for Sustainable Development | ||
| Machine Learning Under Distributional Shifts | ||
| Computational Models of the Neocortex | ||
| From Languages to Information | ||
| Research Seminar in Computer-Generated Music | ||
| Computational Neuroscience | ||
| Logic and Artificial Intelligence | ||
| Topics in Logic, Information and Agency | ||
| Brain decoding | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Theoretical Neuroscience | ||
| Topics in Natural and Artificial Intelligence | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Theories of Consciousness | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Modeling Biomedical Systems | ||
| Representations and Algorithms for Computational Molecular Biology | ||
| Hardware Accelerators for Machine Learning | ||
| General Game Playing | ||
| Deep Generative Models | ||
| Mining Massive Data Sets | ||
| Deep Multi-task and Meta Learning | ||
| Computer Graphics in the Era of AI | ||
| Visual Computing Systems | ||
| Regulating Artificial Intelligence | ||
| Networks | ||
| Data Privacy and Ethics | ||
| Compositional Algorithms, Psychoacoustics, and Computational Music | ||
| Research Seminar in Computer-Generated Music | ||
| Philosophy of Artificial Intelligence | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Modern Applied Statistics: Learning | ||
| Modern Applied Statistics: Data Mining | ||
| Total Units | 15-25 | |
Biomedical Applications
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Biomedical Applications. All courses must be taken for 3 units of more. | ||
| Philosophical and Ethical Inquiry | 3-5 | |
| For example, any of the following: | ||
| Foundations of Bioethics | ||
| Intro to Disability Studies: Disability and Technology | ||
| Social and Ethical Issues in the Neurosciences | ||
| Topics in Philosophy of Medicine | ||
| Phenomenology: Animals | ||
| Philosophy of Neuroscience | ||
| Biological Individuality | ||
| Introduction to Environmental Ethics | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Theories of Consciousness | ||
| The Philosophy and Science of Perception | ||
| Theoretical and Mathematical Approaches | 3-5 | |
| For example, any of the following: | ||
| Theoretical Population Genetics | ||
| Stochastic and Nonlinear Dynamics | ||
| Quantitative Evolutionary Dynamics and Genomics | ||
| Topics in Biomedical Data Science: Large-scale inference | ||
| Mathematical Models and Medical Decisions | ||
| Market Design | ||
| Signal Processing and Linear Systems I | ||
| Introduction to Statistics for the Health Sciences | ||
| Introduction to Health Sciences Statistics | ||
| Principles of Epidemiology | ||
| Health Policy Modeling | ||
| Biostatistics | ||
| Statistical Models in Biology | ||
| Computational and Design Methods | 3-5 | |
| For example, any of the following: | ||
| Artificial Intelligence in Healthcare | ||
| Neuromorphics: Brains in Silicon | ||
| Modeling Biomedical Systems | ||
| Computational Methods for Biomedical Image Analysis and Interpretation | ||
| Deep Learning in Genomics and Biomedicine | ||
| Computational Biology: Structure and Organization of Biomolecules and Cells | ||
| Design for Behavior Change | ||
| Service Design | ||
| The Human Genome Source Code | ||
| Artificial Intelligence for Disease Diagnosis and Information Recommendations | ||
| Computational Models of the Neocortex | ||
| Data Visualization | ||
| Genomics | ||
| Big Data for Biologists - Decoding Genomic Function | ||
| Biology, Health and Big Data | ||
| Topics in Neurodiversity: Design Thinking Approaches | ||
| Computational Neuroimaging | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Cognition in Interaction Design | ||
| Experimental and Observational Science | ||
| For example, any of the following: | ||
| Introduction to Ecology | ||
| Genetics | ||
| Physiology | ||
| Human Behavioral Biology | ||
| Mechanisms of Neuron Death | ||
| Neuroplasticity: From Synapses to Behavior | ||
| Topics in Biomedical Data Science: Large-scale inference | ||
| Big Data for Biologists - Decoding Genomic Function | ||
| Biology, Health and Big Data | ||
| Principles of Epidemiology | ||
| Health Policy Modeling | ||
| The Nervous System | ||
| Brain Plasticity | ||
| Introduction to Perception | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Introduction to Developmental Psychology | ||
| Ion Transport and Intracellular Messengers | ||
| Brain Networks | ||
| Advanced Seminar on Memory | ||
| Cognitive Neuroscience | ||
| Human Neuroimaging Methods | ||
| Brain and Decision | ||
| Affective Neuroscience | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Modeling Biomedical Systems | ||
| Artificial Intelligence in Healthcare | ||
| Computational Methods for Biomedical Image Analysis and Interpretation | ||
| The Human Genome Source Code | ||
| Deep Learning in Genomics and Biomedicine | ||
| Computational Biology: Structure and Organization of Biomolecules and Cells | ||
| Advanced Topics in Human Virtual Representation | ||
| Data for Sustainable Development | ||
| Artificial Intelligence for Disease Diagnosis and Information Recommendations | ||
| Computational Models of the Neocortex | ||
| Philosophy of Neuroscience | ||
| Biological Individuality | ||
| Introduction to Environmental Ethics | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Topics in Neurodiversity: Design Thinking Approaches | ||
| Ion Transport and Intracellular Messengers | ||
| Brain Networks | ||
| Advanced Seminar on Memory | ||
| Cognitive Neuroscience | ||
| Human Neuroimaging Methods | ||
| Computational Neuroimaging | ||
| Brain and Decision | ||
| Affective Neuroscience | ||
| Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Cognition in Interaction Design | ||
| Contingent Electives | ||
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Cognitive Science
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Cognitive Science. All courses must be taken for 3 units of more. | ||
| Cognitive Neuroscience | 3-5 | |
| Select one of the following: | ||
| Introduction to Perception | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Inferential Statistics | 3-5 | |
| Select one of the following: | ||
| Data Analysis for Quantitative Research | ||
| Introduction to Applied Statistics | ||
| Fundamentals of Data Science: Prediction, Inference, Causality | ||
| Introduction to Statistical Methods: Precalculus | ||
| Advanced Statistical Modeling | ||
| Introduction to Data Analysis | ||
| Data Science 101 | ||
| Statistical Methods in Engineering and the Physical Sciences | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Research Methods | 3-5 | |
| A course on research practices and/or methods that are commonly used for studying cognition, language, and the brain. For example, one of the following: | ||
| Computer Organization and Systems | ||
| Applied Machine Learning | ||
| Machine Learning | ||
| From Languages to Information | ||
| Natural Language Understanding | ||
| Methods in Psycholinguistics | ||
| Philosophy of Neuroscience | ||
| Brain decoding | ||
| Research Methods in Cognition & Development | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Image Systems Engineering | ||
| Curiosity in Artificial Intelligence | ||
| Theoretical Neuroscience | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Experimental Methods | ||
| Advanced Statistical Modeling | ||
| Measurement and the Study of Change in Social Science Research | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Cognitive Science Depth | 3-5 | |
| For example, one of the following courses: | ||
| Human Behavioral Biology | ||
| Media Processes and Effects | ||
| Advanced Studies in Behavior and Social Media | ||
| Computer Vision: Foundations and Applications | ||
| Introduction to the Theory of Computation | ||
| Natural Language Processing with Deep Learning | ||
| General Game Playing | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning Theory | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Reinforcement Learning | ||
| Decision Making under Uncertainty | ||
| Game Theory and Economic Applications | ||
| Educational Neuroscience | ||
| Cognitive Development in Childhood and Adolescence | ||
| Phonetics | ||
| Introduction to Phonology | ||
| Learning to Speak: An Introduction to Child Language Acquisition | ||
| From Languages to Information | ||
| Natural Language Understanding | ||
| Seminar in Semantics: Conditionals | ||
| Seminar in Developmental Psycholinguistics | ||
| Psychophysics and Music Cognition | ||
| The Nervous System | ||
| Information and Signaling Mechanisms in Neurons and Circuits | ||
| Philosophy of Cognitive Science | ||
| Computability and Logic | ||
| Computing Machines and Intelligence | ||
| Modal Logic | ||
| Philosophy of Neuroscience | ||
| Philosophy of Language | ||
| Topics in Epistemology | ||
| Formal Epistemology | ||
| Philosophy of Mind | ||
| Philosophy of Action | ||
| Rationality Over Time | ||
| Capstone Seminar: Artificial Intelligence | ||
| Measurement Theory | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Advanced Topics in Philosophy of Language | ||
| Truth as the aim of belief and inquiry | ||
| Introduction to Perception | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Self and Society: Introduction to Social Psychology | ||
| Introduction to Cultural Psychology | ||
| Introduction to Psycholinguistics | ||
| Cognitive Development | ||
| Judgment and Decision-Making | ||
| Seminar on Emotion | ||
| Brain Networks | ||
| Brain decoding | ||
| Advanced Seminar on Memory | ||
| Social Cognition and Learning in Early Childhood | ||
| Cognitive Neuroscience | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Human Neuroimaging Methods | ||
| Computational Neuroimaging | ||
| Foundations of Cognition | ||
| Neural Network Models of Cognition | ||
| Image Systems Engineering | ||
| Brain and Decision | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| High-level Vision: From Neurons to Deep Neural Networks | ||
| Affective Neuroscience | ||
| Current Debates in Learning and Memory | ||
| Graduate Seminar on Theory of Mind | ||
| Brain Machine Interfaces: Science, Technology, and Application | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Cognitive Science Perspectives on Humanity and Well-Being | ||
| Conceptual Issues in Cognitive Science | ||
| Computer Machines and Intelligence | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Advanced Topics in Human Virtual Representation | ||
| Computer Vision: Foundations and Applications | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Artificial Intelligence: Principles and Techniques | ||
| General Game Playing | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Deep Learning | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Reinforcement Learning | ||
| Decision Making under Uncertainty | ||
| Data for Sustainable Development | ||
| Computational Models of the Neocortex | ||
| Introduction to Machine Learning | ||
| From Languages to Information | ||
| Research Seminar in Computer-Generated Music | ||
| Neuroplasticity and Musical Gaming | ||
| Social and Ethical Issues in the Neurosciences | ||
| Phenomenology: Animals | ||
| Logic and Artificial Intelligence | ||
| Research Seminar on Logic and Cognition | ||
| Topics in Logic, Information and Agency | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Brain decoding | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Theoretical Neuroscience | ||
| Topics in Natural and Artificial Intelligence | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Theories of Consciousness | ||
| The Philosophy and Science of Perception | ||
| Conceptual Issues in Cognitive Science | ||
| Computer Machines and Intelligence | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Computational Foundations
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Computational Foundations. All courses must be taken for 3 units of more. | ||
| Students in this Concentration are strongly encouraged to take either CS 181 or CS 182 as part of the major, either for the Core Cross-Area Requirement, for the Capstone Integrative Requirement, as a Contingent Elective, or (in the case of CS 182) for the Core Introductory Philosophy requirement. | ||
| Computer Systems I | 3-5 | |
| Select one of the following: | ||
| Computer Organization and Systems | ||
| Computer Systems from the Ground Up | ||
| Computer Systems II | 3-5 | |
| Select one of the following: | ||
| Principles of Computer Systems | ||
| Operating Systems Principles | ||
| Theory of Computation Depth | 3-5 | |
| Select one of the following: | ||
| Introduction to the Theory of Computation | ||
| Modal Logic | ||
| Algorithms | 3-5 | |
| Design and Analysis of Algorithms | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Logic Programming | ||
| Computational Logic | ||
| The Practice of Theory Research | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Project Lab: Video and Audio Technology for Live Theater in the Age of COVID | ||
| Computational Models of the Neocortex | ||
| Modal Logic | ||
| Topics in Logic, Information and Agency | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Any course of 3 units or more, listed with an EE course number. | ||
Any course of 3 units or more, listed with a MATH course number. | ||
Any course of 3 units or more, listed with a STATS course number. | ||
| Philosophy of Artificial Intelligence | ||
| Total Units | 15-25 | |
Computational Social Science Concentration
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Computational Social Science. All courses must be taken for 3 units of more. | ||
| Social Behavior | 3-5 | |
| An introductory course in a broad area of social science. Select one of the following: | ||
| Ecology for Everyone | ||
| Introduction to Ecology | ||
| Introduction to Communication | ||
| Principles of Economics | ||
| Networks and Human Behavior | ||
| Economic Analysis I | ||
| Game Theory and Economic Applications | ||
| Behavioral Economics | ||
| Honors Game Theory | ||
| Language and Society | ||
| Networks | ||
| Organizations: Theory and Management | ||
| Introduction to Game Theory | ||
| The Science of Politics | ||
| American Political Institutions in Uncertain Times | ||
| Self and Society: Introduction to Social Psychology | ||
| Judgment and Decision-Making | ||
| Introduction to Sociology | ||
| Introduction to Social Networks | ||
| Education and Society | ||
| Introduction to Comparative Studies in Race and Ethnicity | ||
| Statistical Interference | 3-5 | |
| An introductory course in statistical methods. Select one of the following: | ||
| Introduction to Statistical Methods (Postcalculus) for Social Scientists | ||
| Introduction to Applied Statistics | ||
| Fundamentals of Data Science: Prediction, Inference, Causality | ||
| Introduction to Data Analysis | ||
| Statistical Methods in Engineering and the Physical Sciences | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Computational Data Methods | 3-5 | |
| A course in machine learning, natural language processing, and/or probabilistic computational inference. Select one of the following: | ||
| Applied Machine Learning | ||
| Natural Language Processing with Deep Learning | ||
| Machine Learning with Graphs | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Deep Learning | ||
| Decision Making under Uncertainty | ||
| Mining Massive Data Sets | ||
| Data Visualization | ||
| Applied Econometrics | ||
| From Languages to Information | ||
| Natural Language Understanding | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Introduction to Statistical Learning | ||
| Social Data Science | 3-5 | |
| A course on applying statistical and computational methods to the study of social behavior. Select one of the following: | ||
| Communication Research Methods | ||
| Data Challenge Lab | ||
| Econometric Methods for Public Policy Analysis and Business Decision-Making | ||
| Tackling Big Questions Using Social Data Science | ||
| Introduction to Data Science | ||
| Introduction to Computational Social Science | ||
| Data Science for Politics | ||
| Causal Inference for Social Science | ||
| Natural Language Processing & Text-Based Machine Learning in the Social Sciences | ||
| Foundations of Social Research | ||
| Computational Undergraduate Research | ||
| Social Network Methods | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Advanced Studies in Behavior and Social Media | ||
| Advanced Topics in Human Virtual Representation | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Exploring Computational Journalism | ||
| Machine Learning with Graphs | ||
| Mining Massive Data Sets | ||
| Social Computing | ||
| Data for Sustainable Development | ||
| Game Theory and Economic Applications | ||
| Behavioral Economics | ||
| Honors Game Theory | ||
| Data Privacy and Ethics | ||
| Justice | ||
| 20th Century Political Theory: Liberalism and its Critics | ||
| Topics in Logic, Information and Agency | ||
| Judgment and Decision-Making | ||
| Measurement and the Study of Change in Social Science Research | ||
| Natural Language Processing & Text-Based Machine Learning in the Social Sciences | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| The Politics of Algorithms | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Data Analysis for Quantitative Research | ||
| Thinking Technology: Anthropological Perspectives | ||
| Science as a Creative Process | ||
| Evolution | ||
| Ecology and Evolution of Animal Behavior | ||
| Communication Research Methods | ||
| Censorship and Propaganda | ||
| Data Challenge Lab | ||
| Advanced Digital Media Journalism | ||
| Big Local Journalism: a project-based class | ||
| Programming in Journalism | ||
| Building News Applications | ||
| Data Management and Data Systems | ||
| Topics in Advanced Robotic Manipulation | ||
| Applied Econometrics | ||
| Advanced Topics in Econometrics | ||
| World Food Economy | ||
| Development Economics | ||
| Economic Development, Microfinance, and Social Networks | ||
| Family and Society | ||
| Economic Policy Analysis | ||
| Environmental Economics and Policy | ||
| Advanced Statistical Methods for Observational Studies | ||
| Language, Gender, & Sexuality | ||
| Sociophonetics | ||
| The Structure of Discourse: Theory and Applications | ||
| Sociolinguistic Theory and Analysis | ||
| Analysis of Variation | ||
| Programming for Linguists | ||
| Spoken Language Processing | ||
| Machine Learning and Causal Inference | ||
| Introduction to Stochastic Modeling | ||
| Introduction to Applied Statistics | ||
| Future of Work: Issues in Organizational Learning and Design | ||
| Dynamic Systems | ||
| Stochastic Modeling | ||
| Simulation | ||
| Incentives and Algorithms | ||
| Introduction to Computational Social Science | ||
| Data Privacy and Ethics | ||
| Energy and Environmental Policy Analysis | ||
| Organizational Behavior: Evidence in Action | ||
| Health Policy Modeling | ||
| Introduction to Moral Philosophy | ||
| Introduction to Philosophy of Science | ||
| Ethical Theory | ||
| Justice | ||
| 20th Century Political Theory: Liberalism and its Critics | ||
| Universal Basic Income: the philosophy behind the proposal | ||
| Philosophy of Public Policy | ||
| The Science of Politics | ||
| What's Wrong with American Government? An Institutional Approach | ||
| Data Science for Politics | ||
| Causal Inference for Social Science | ||
| Public Opinion and American Democracy | ||
| Money in Politics | ||
| Political Economy of Development | ||
| Spatial Approaches to Social Science | ||
| Psychology of Xenophobia | ||
| Neuroforecasting | ||
| Introduction to Sociology | ||
| America: Unequal | ||
| Introduction to Computational Social Science | ||
| Inequality in American Society | ||
| Social Networks | ||
| Economic Sociology | ||
| Social Movements and Collective Action | ||
| Gender and Technology | ||
| Education and Society | ||
| Globalization and Social Change | ||
| Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt | ||
| Global Organizations: The Matrix of Change | ||
| Crime and Punishment in America | ||
| Data Science 101 | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Meta-research: Appraising Research Findings, Bias, and Meta-analysis | ||
| Doing STS: Introduction to Research | ||
| Digital Technology, Society, and Democracy | ||
| Total Units | 15-25 | |
Computer Music
See also the Symbolic Systems website.
| Symbolic Systems majors must complete the following requirements in addition to the Core requirements to fulfill the Concentration in Computer Music. All courses must be taken for 3 units of more. | ||
| Computer-Generated Music I | 3-5 | |
| Fundamentals of Computer-Generated Sound | ||
| Computer-Generated Music II | 3-5 | |
| Compositional Algorithms, Psychoacoustics, and Computational Music | ||
| Music and the Mind & Brain | 3-5 | |
| Select one of the following: | ||
| Music, Mind, and Human Behavior | ||
| Psychophysics and Music Cognition | ||
| Seminar in Music Perception and Cognition I | ||
| Introduction to Perception | ||
| Introduction to Cognitive Neuroscience | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Stanford Laptop Orchestra: Composition, Coding, and Performance | ||
| Research Seminar in Computer-Generated Music | ||
| Physical Interaction Design for Music | ||
| Psychophysics and Music Cognition | ||
| Symbolic Musical Information | ||
| Computational Music Analysis | ||
| Music, Computing, Design: The Art of Design | ||
| Neuroplasticity and Musical Gaming | ||
| Seminar in Music Perception and Cognition I | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Object-Oriented Systems Design | ||
| Phonetics | ||
| Introduction to Phonology | ||
| Music, Mind, and Human Behavior | ||
| Sound in Space | ||
| Total Units | 15-25 | |
Decision Making and Rationality (DMAR)
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Decision Making and Rationality. All courses must be taken for 3 units of more. | ||
| Philosophical Inquiry | 3-5 | |
| Select one of the following: | ||
| Data Privacy and Ethics | ||
| The Ethical Analyst | ||
| Central Topics in the Philosophy of Science: Theory and Evidence | ||
| Probability: Ten Great Ideas About Chance | ||
| Evolution of the Social Contract | ||
| Ethical Theory | ||
| Justice | ||
| History of Modern Moral Philosophy | ||
| Topics in Epistemology | ||
| Formal Epistemology | ||
| Philosophy of Action | ||
| Topics in Logic, Information and Agency | ||
| Topics in Normativity | ||
| Modern Political Thought: Machiavelli to Marx and Mill | ||
| Classical Seminar: Origins of Political Thought | ||
| Seminar on Emotion | ||
| Formal Decision Theories | 3-5 | |
| Select one of the following: | ||
| Economic Analysis II | ||
| Market Design | ||
| Game Theory and Economic Applications | ||
| Honors Game Theory | ||
| Advanced Topics in Game Theory and Information Economics | ||
| Introduction to Game Theory | ||
| Introduction to Game Theory | ||
| Modal Logic | ||
| Representation Theorems | ||
| Formal Methods in Ethics | ||
| Measurement Theory | ||
| Topics in Logic, Information and Agency | ||
| Formal Theory I: Game Theory for Political Science | ||
| Microeconomics for Policy | ||
| Empirical Findings and Explanations | 3-5 | |
| Select one of the following: | ||
| Human Behavioral Biology | ||
| Behavioral Economics | ||
| Experimental Economics | ||
| Behavioral and Experimental Economics II | ||
| Seminar on Organizational Theory | ||
| Behavioral Economics and the Psychology of Decision Making | ||
| Economic Analysis of Political Institutions | ||
| Institutions and Bridge-Building in Political Economy | ||
| Judgment and Decision-Making | ||
| Seminar on Emotion | ||
| Foundations of Cognition | ||
| Classic and contemporary social psychology research | ||
| Mind, Culture, and Society | ||
| Social Norms | ||
| Brain and Decision | ||
| Affective Neuroscience | ||
| Economic Sociology | ||
| Topics in Economic Sociology | ||
| Introduction to Social Networks | ||
| Cognitive Science Perspectives on Humanity and Well-Being | ||
| Methods and Applications | 3-5 | |
| A course on methods that can be used to study decision making and rationality, or ways to apply research in decision sciences. For example, one of the following: | ||
| Outcomes Analysis | ||
| Decision Analysis for Civil and Environmental Engineers | ||
| Communication Research Methods | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Reinforcement Learning | ||
| Decision Making under Uncertainty | ||
| Advanced Topics in Sequential Decision Making | ||
| Optimization and Algorithmic Paradigms | ||
| Data for Sustainable Development | ||
| Economic Analysis I | ||
| Applied Econometrics | ||
| Advanced Topics in Econometrics | ||
| Foundations of Finance | ||
| Market Design | ||
| Decision Modeling and Information | ||
| Public Finance and Fiscal Policy | ||
| Economic Policy Analysis | ||
| Environmental Economics and Policy | ||
| Games Developing Nations Play | ||
| Moral and Character Education | ||
| Introduction to Optimization | ||
| Introduction to Stochastic Modeling | ||
| Introduction to Decision Analysis | ||
| Organizations: Theory and Management | ||
| Introduction to Computational Social Science | ||
| Engineering Risk Analysis | ||
| Project Course in Engineering Risk Analysis | ||
| Introduction to Stochastic Control with Applications | ||
| Decision Analysis I: Foundations of Decision Analysis | ||
| Security and Risk in Computer Networks | ||
| Decision Analysis II: Professional Decision Analysis | ||
| Decision Analysis III: Frontiers of Decision Analysis | ||
| Influence Diagrams and Probabilistics Networks | ||
| Survey of Formal Methods | ||
| Thinking Strategically | ||
| Introduction to Statistical Methods: Precalculus | ||
| Experimental Methods | ||
| Advanced Statistical Modeling | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Meta-research: Appraising Research Findings, Bias, and Meta-analysis | ||
| Introduction to Stochastic Processes I | ||
| Introduction to Stochastic Processes II | ||
| Design of Experiments | ||
| Theory of Probability I | ||
| Theory of Probability II | ||
| Theory of Probability III | ||
| Design for Behavior Change | ||
| Research in Digital Democracy | ||
| Digital Technology, Society, and Democracy | ||
| Concepts and Analytic Skills for the Social Sector | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Reinforcement Learning | ||
| Decision Making under Uncertainty | ||
| Advanced Topics in Sequential Decision Making | ||
| Optimization and Algorithmic Paradigms | ||
| Data for Sustainable Development | ||
| Topics in Epistemology | ||
| Formal Epistemology | ||
| Philosophy of Action | ||
| Topics in Logic, Information and Agency | ||
| Judgment and Decision-Making | ||
| Seminar on Emotion | ||
| Social Norms | ||
| Brain and Decision | ||
| Affective Neuroscience | ||
| Digital Technology, Society, and Democracy | ||
| Cognitive Science Perspectives on Humanity and Well-Being | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Econometric Methods for Public Policy Analysis and Business Decision-Making | ||
| Tackling Big Questions Using Social Data Science | ||
| How We Decide: Social Choice in the Age of Algorithms | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Human-Centered Artificial Intelligence Concentration
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Human-Centered Artificial Intelligence. All courses must be taken for 3 units of more. | ||
| Digital Technology Ethics and Policy | 3-5 | |
| Select one of the following: | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Human Impact | 3-5 | |
| One course aimed at understanding how AI interacts with humans as well as with vital social structures and institutions. For example: | ||
| Funkentelechy: Technologies, Social Justice and Black Vernacular Cultures | ||
| Thinking Technology: Anthropological Perspectives | ||
| Whose Ghost in the Machine? Cultures, Politics and Morals of Artificial Intelligence | ||
| The Rise of Digital Culture | ||
| Truth, Trust, and Tech | ||
| Personality and Digital Media | ||
| The Politics of Algorithms | ||
| Media Psychology | ||
| Race and Media | ||
| Advanced Studies in Behavior and Social Media | ||
| Law, Order, & Algorithms | ||
| A.I.-Activism-Art | ||
| Politics of Data: Algorithmic Culture, Big Data, and Information Waste | ||
| Regulating Artificial Intelligence | ||
| Digital Technology and Law: Foundations | ||
| AI and Rule of Law: A Global Perspective | ||
| Future of Work: Issues in Organizational Learning and Design | ||
| Data Privacy and Ethics | ||
| Social and Ethical Issues in the Neurosciences | ||
| Universal Basic Income: the philosophy behind the proposal | ||
| Gender and Technology | ||
| The Public Life of Science and Technology | ||
| Digital Technology, Society, and Democracy | ||
| Augmenting Human Capabilities | 3-5 | |
| One course aimed at developing new human-centered design methods and tools so that AI agents and applications are designed and created with the ability to communicate with, collaborate with, and augment people more effectively, and to make their work better and more enjoyable. For example: | ||
| Artificial Intelligence in Healthcare | ||
| Virtual People | ||
| Big Local Journalism: a project-based class | ||
| Advanced Topics in Human Virtual Representation | ||
| Introduction to Human-Computer Interaction Design | ||
| Trust and Safety Engineering | ||
| Bridging Policy and Tech Through Design | ||
| Design for Artificial Intelligence | ||
| Design for Behavior Change | ||
| Design for Understanding | ||
| Service Design | ||
| Social Computing | ||
| Data for Sustainable Development | ||
| Fair, Accountable, and Transparent (FAccT) Deep Learning | ||
| Artificial Intelligence for Disease Diagnosis and Information Recommendations | ||
| Data Visualization | ||
| Market Design | ||
| Beyond Bits and Atoms - Lab | ||
| Beyond Bits and Atoms: Designing Technological Tools | ||
| Educational Neuroscience | ||
| Technology for Learners | ||
| Behavior Design: Clubhouse for Helping People with Good Habits & Behavior Change | ||
| Designing AI to Cultivate Human Well-Being | ||
| Body Hacking: Applied Topics in Exercise Physiology | ||
| Biology, Health and Big Data | ||
| Research Seminar in Computer-Generated Music | ||
| Brain Plasticity | ||
| Topics in Neurodiversity: Design Thinking Approaches | ||
| Designing for the 2 Billion: Leading Innovation in Mental Health | ||
| Neuroforecasting | ||
| Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement | ||
| Natural Language Processing & Text-Based Machine Learning in the Social Sciences | ||
| Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt | ||
| Cognition in Interaction Design | ||
| Intelligence | 3-5 | |
| One course aimed at developing machine intelligence that understands human language, emotions, intentions, behaviors, and interactions at multiple scales. One of the following: | ||
| Applied Machine Learning | ||
| Computer Vision: Foundations and Applications | ||
| Artificial Intelligence: Principles and Techniques | ||
| Introduction to Robotics | ||
| Natural Language Processing with Deep Learning | ||
| Machine Learning | ||
| Deep Learning | ||
| Natural Language Understanding | ||
| Spoken Language Processing | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Virtual People | ||
| Media Psychology | ||
| Exploring Computational Journalism | ||
| Artificial Intelligence: Principles and Techniques | ||
| Introduction to Robotics | ||
| Machine Learning | ||
| Deep Learning | ||
| Decision Making under Uncertainty | ||
| Design for Understanding | ||
| Social Computing | ||
| Data for Sustainable Development | ||
| Fair, Accountable, and Transparent (FAccT) Deep Learning | ||
| Artificial Intelligence for Disease Diagnosis and Information Recommendations | ||
| Computational Models of the Neocortex | ||
| Curiosity in Artificial Intelligence | ||
| Educational Neuroscience | ||
| Technology for Learners | ||
| From Languages to Information | ||
| Spoken Language Processing | ||
| Philosophy of Neuroscience | ||
| Biological Individuality | ||
| Topics in Logic, Information and Agency | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Advanced Topics in Philosophy of Language | ||
| Truth as the aim of belief and inquiry | ||
| Brain Plasticity | ||
| Topics in Neurodiversity: Design Thinking Approaches | ||
| Designing for the 2 Billion: Leading Innovation in Mental Health | ||
| Ion Transport and Intracellular Messengers | ||
| Seminar on Infant Development | ||
| Judgment and Decision-Making | ||
| Brain Networks | ||
| Brain decoding | ||
| Advanced Seminar on Memory | ||
| Cognitive Neuroscience | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Brain and Decision | ||
| Theoretical Neuroscience | ||
| Topics in Natural and Artificial Intelligence | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Affective Neuroscience | ||
| Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Introduction to Statistical Learning | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Modern Applied Statistics: Data Mining | ||
| Theories of Consciousness | ||
| The Philosophy and Science of Perception | ||
| Cognition in Interaction Design | ||
| Contingent Electives | ||
| If requirements 1-4 are fulfilled partly from courses taken for Core requirements, then additional approved Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Philosophy of Artificial Intelligence | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Total Units | 15-25 | |
Human-Computer Interaction
See also the Symbolic Systems website.
| Symbolic Systems majors must complete the following requirements in addition to the Core requirements to fulfill the Concentration in Human-Computer Interaction. All courses must be taken for 3 units of more. | ||
| Students in this Concentration are urged to take CS 107 or CS 107E, either for the Post-CS 106B Computation Core requirement, or as a Contingent Elective, and prior to completing requirement 4 below. | ||
| Introduction to HCI | 3-5 | |
| Introduction to Human-Computer Interaction Design | ||
| Design Methods | 3-5 | |
| Post-CS 147 courses teaching fundamentals of the human-centered design process, featuring a major project component (including any course in the CS 247 series). One of the following: | ||
| User Interface Design Project | ||
| Design for Artificial Intelligence | ||
| Design for Behavior Change | ||
| Introduction to Game Design | ||
| Service Design | ||
| HCI Theory | 3-5 | |
| Courses teaching design, behavioral, and critical theories that underlie the design process. One of the following: | ||
| Personality and Digital Media | ||
| Virtual People | ||
| Media Psychology | ||
| Human-Computer Interaction: Foundations and Frontiers | ||
| Design Experiments | ||
| User Interface Implementation | 3-5 | |
| An advanced course in programming for user interfaces. One of the following: | ||
| Object-Oriented Systems Design | ||
| Web Applications | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| The Rise of Digital Culture | ||
| Personality and Digital Media | ||
| Virtual People | ||
| Media Psychology | ||
| Advanced Studies in Behavior and Social Media | ||
| Language and Technology | ||
| Advanced Topics in Human Virtual Representation | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Exploring Computational Journalism | ||
| Design for Understanding | ||
| Social Computing | ||
| Human-Computer Interaction: Foundations and Frontiers | ||
| Designing Solutions to Global Grand Challenges | ||
| Designing Serious Games | ||
| Designing for Accessibility | ||
| Understanding Users | ||
| Learning Experience Design | ||
| Technology for Learners | ||
| Behavior Design: Clubhouse for Helping People with Good Habits & Behavior Change | ||
| Child Development and New Technologies | ||
| Engineering Education and Online Learning | ||
| Product Design Methods | ||
| Design Experiments | ||
| Digital Technology, Society, and Democracy | ||
| Cognition in Interaction Design | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Any d.school course worth 3 or more units | ||
| Interactive Art: Making it with Arduino | ||
| Mixed-Media Drawing: Art & Aesthetics of Social Media | ||
| Intro to Digital / Physical Design | ||
| Data as Material | ||
| Digital Art I | ||
| Introduction to Communication | ||
| Media, Culture, and Society | ||
| Communication Research Methods | ||
| Truth, Trust, and Tech | ||
| The Politics of Algorithms | ||
| Digital Civil Society | ||
| Digital Civil Society | ||
| Digital Civil Society | ||
| Ethnographic Methods | ||
| Race and Gender in Silicon Valley | ||
| Introduction to Survey Research | ||
| Introduction to Data Science | ||
| Data Challenge Lab | ||
| Qualitative Research Methodology | ||
| Visual Thinking | ||
| Designing for Impact | ||
| Introduction to Human Values in Design | ||
| Design and Manufacturing | ||
| Introduction to Mechatronics | ||
| Advanced Product Design: Needfinding | ||
| Methods in Community Assessment, Evaluation, and Research | ||
| Biodesign Fundamentals | ||
| Introduction to Applied Statistics | ||
| Networks | ||
| Data Privacy and Ethics | ||
| Introduction to Aesthetics | ||
| Introduction to Statistical Methods: Precalculus | ||
| Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt | ||
| Data Science 101 | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Design of Experiments | ||
| The Public Life of Science and Technology | ||
| Total Units | 15-25 | |
Learning
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Learning. All courses must be taken for 3 units of more. | ||
| Students in the Learning Concentration must complete four courses from areas 1-3 below with at least one from each area, plus one course from area 4. If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses (see area 5) must be completed to total 5 courses beyond those that are taken for the Core. | ||
| Computational Learning | 3-5 | |
| Continuous Mathematical Methods with an Emphasis on Machine Learning | ||
| Artificial Intelligence: Principles and Techniques | ||
| Natural Language Processing with Deep Learning | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Machine Learning Theory | ||
| Deep Learning | ||
| Reinforcement Learning | ||
| Deep Generative Models | ||
| Data for Sustainable Development | ||
| Introduction to Machine Learning | ||
| From Languages to Information | ||
| Data Privacy and Ethics | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Data Science 101 | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Modern Applied Statistics: Learning | ||
| Modern Applied Statistics: Data Mining | ||
| Human Learning | 3-5 | |
| Introduction to Teaching and Learning | ||
| How to Learn Mathematics | ||
| Topics in Cognition and Learning: Technology and Multitasking | ||
| Educational Neuroscience | ||
| Cognitive Development in Childhood and Adolescence | ||
| Social and Emotional Learning: Conceptual & Measurement Issues | ||
| Learning to Speak: An Introduction to Child Language Acquisition | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Introduction to Developmental Psychology | ||
| Cognitive Development | ||
| Advanced Seminar on Memory | ||
| Social Cognition and Learning in Early Childhood | ||
| Cognitive Neuroscience | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Experimental Methods | ||
| Social Psychology and Social Change | ||
| Current Debates in Learning and Memory | ||
| Learning Environment Design | 3-5 | |
| Advanced Studies in Behavior and Social Media | ||
| Introduction to Human-Computer Interaction Design | ||
| User Interface Design Project | ||
| Beyond Bits and Atoms - Lab | ||
| Learning Experience Design | ||
| Beyond Bits and Atoms: Designing Technological Tools | ||
| Technology for Learners | ||
| Seminar on Teaching Introductory Computer Science | ||
| Designing Learning Spaces | ||
| Topics in Learning and Technology: Core Mechanics for Learning | ||
| Understanding Learning Environments | ||
| Child Development and New Technologies | ||
| Engineering Education and Online Learning | ||
| Unleashing Personal Potential: Behavioral Science and Design Thinking Applied to Self | ||
| Neuroplasticity and Musical Gaming | ||
| Brain Machine Interfaces: Science, Technology, and Application | ||
| Cognition in Interaction Design | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Advanced Topics in Human Virtual Representation | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Artificial Intelligence: Principles and Techniques | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Machine Learning Theory | ||
| Deep Learning | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Reinforcement Learning | ||
| Computational Models of the Neocortex | ||
| Topics in Epistemology and Education | ||
| Curriculum and Instruction Elective in Data Science | ||
| Introduction to Machine Learning | ||
| From Languages to Information | ||
| Formal Epistemology | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Theoretical Neuroscience | ||
| Topics in Natural and Artificial Intelligence | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Measurement and the Study of Change in Social Science Research | ||
| Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Cognition in Interaction Design | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Introduction to Statistical Methods: Precalculus | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Mathematical Foundations
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Mathematical Foundations. All courses must be taken for 3 units of more. | ||
| Multivariate Calculus and Linear Algebra | 10 | |
| One of the following two-course sequences (Note: The earlier courses in each series are included in the Core Preparations requirements. Students in this Concentration who began in the CME 100 series should switch to the MATH 52-MATH 53 series for the Concentration.) | ||
| Matrix Theory and Applications | 3-5 | |
| Select one of the following: | ||
| Continuous Mathematical Methods with an Emphasis on Machine Learning | ||
| Linear Algebra and Matrix Theory | ||
| Applied Mathematics and Statistics | 3-5 | |
| Select one of the following: | ||
| Introduction to Machine Learning | ||
| Introduction to Linear Dynamical Systems | ||
| Machine Learning Theory | ||
| Introduction to Linear Dynamical Systems | ||
| Information Theory | ||
| Introduction to Combinatorics and Its Applications | ||
| Applied Number Theory and Field Theory | ||
| Stochastic Processes | ||
| Basic Probability and Stochastic Processes with Engineering Applications | ||
| Discrete Probabilistic Methods | ||
| Introduction to Optimization | ||
| Introduction to Optimization (Accelerated) | ||
| Introduction to Stochastic Modeling | ||
| Dynamic Systems | ||
| Introduction to Optimization Theory | ||
| Stochastic Modeling | ||
| Advanced Statistical Modeling | ||
| Statistical Methods in Engineering and the Physical Sciences | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Introduction to Statistical Learning | ||
| Introduction to Stochastic Processes I | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Applied Machine Learning | ||
| Logic Programming | ||
| Introduction to the Theory of Computation | ||
| Computational Logic | ||
| Design and Analysis of Algorithms | ||
| The Practice of Theory Research | ||
| Continuous Mathematical Methods with an Emphasis on Machine Learning | ||
| Machine Learning with Graphs | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Machine Learning Theory | ||
| Deep Learning | ||
| Mining Massive Data Sets | ||
| Computational Complexity | ||
| Introduction to Cryptography | ||
| Quantum Computing | ||
| Data for Sustainable Development | ||
| Computational Models of the Neocortex | ||
| Game Theory and Economic Applications | ||
| Behavioral Economics | ||
| Honors Game Theory | ||
| Introduction to Scientific Computing | ||
| Decision Analysis I: Foundations of Decision Analysis | ||
| Metalogic | ||
| Computability and Logic | ||
| Modal Logic | ||
| Topics in Mathematical Logic: Non-Classical Logic | ||
| Philosophy of Mathematics | ||
| Formal Epistemology | ||
| Seminar on Philosophy of Logic and Mathematics | ||
| Topics in Logic, Information and Agency | ||
| Judgment and Decision-Making | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Computational Neuroimaging | ||
| Neural Network Models of Cognition | ||
| Brain and Decision | ||
| Theoretical Neuroscience | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Advanced Statistical Modeling | ||
| The Politics of Algorithms | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Contingent Electives | ||
| If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Mathematical Models and Medical Decisions | ||
| Algebraic Error Correcting Codes | ||
| Computational Complexity II | ||
| Counting and Sampling | ||
| Information Theory and Statistics | ||
| Proofs and Modern Mathematics | ||
| Graph Theory | ||
| Functions of a Real Variable | ||
| Groups and Rings | ||
| Introduction to Topology and Geometry | ||
| Elementary Theory of Numbers | ||
| Fundamental Concepts of Analysis | ||
| Randomness: Computational and Philosophical Approaches | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Applied Multivariate Analysis | ||
| Introduction to Stochastic Processes II | ||
| Random Processes on Graphs and Lattices | ||
| Total Units | 15-25 | |
Media and Communication Concentration
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Media and Communication. All courses must be taken for 3 units of more | ||
| Introduction | 3-5 | |
| Introduction to Communication | ||
| Statistical and Data Analysis Methods | 3-5 | |
| Select one of the following: | ||
| Data Analysis for Quantitative Research | ||
| Data Challenge Lab | ||
| Machine Learning | ||
| Introduction to Applied Statistics | ||
| Fundamentals of Data Science: Prediction, Inference, Causality | ||
| Advanced Statistical Modeling | ||
| Introduction to Data Analysis | ||
| Introduction to Statistical Methods: Precalculus | ||
| Data Science 101 | ||
| Statistical Methods in Engineering and the Physical Sciences | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Data Mining and Analysis | ||
| Research Methods | 3-5 | |
| A course on empirical and computational methods that are commonly used for research on media and communication. One of the following: | ||
| Communication Research Methods | ||
| Web Applications | ||
| Introduction to Human-Computer Interaction Design | ||
| Human-Computer Interaction: Foundations and Frontiers | ||
| Data Visualization | ||
| Intersectional Qualitative Approaches | ||
| Introduction to Data Science | ||
| Introduction to Qualitative Research Methods | ||
| Beyond Bits and Atoms - Lab | ||
| Beyond Bits and Atoms: Designing Technological Tools | ||
| Qualitative Research Methodology | ||
| Design Experiments | ||
| Networks | ||
| Introduction to Computational Social Science | ||
| Optimization of Uncertainty and Applications in Finance | ||
| Introduction to Philosophy of Science | ||
| Data Science for Politics | ||
| Causal Inference for Social Science | ||
| Experimental Methods | ||
| Introduction to Computational Social Science | ||
| Foundations of Social Research | ||
| Computational Undergraduate Research | ||
| Social Network Methods | ||
| Meta-research: Appraising Research Findings, Bias, and Meta-analysis | ||
| Doing STS: Introduction to Research | ||
| Effects, Ethics, and Policy | 3-5 | |
| A course on the effects of, and possible responses to, digital technology, media, and communication. For example, one of the following: | ||
| Funkentelechy: Technologies, Social Justice and Black Vernacular Cultures | ||
| Thinking Technology: Anthropological Perspectives | ||
| Whose Ghost in the Machine? Cultures, Politics and Morals of Artificial Intelligence | ||
| Media, Culture, and Society | ||
| Media Processes and Effects | ||
| The Rise of Digital Culture | ||
| Truth, Trust, and Tech | ||
| Perspectives on American Journalism | ||
| Deliberative Democracy and its Critics | ||
| Personality and Digital Media | ||
| Free Speech, Democracy and the Internet | ||
| The Politics of Algorithms | ||
| Campaigns, Voting, Media, and Elections | ||
| The Psychology of Communication About Politics in America | ||
| Virtual People | ||
| Media Psychology | ||
| Ethics, Public Policy, and Technological Change | ||
| Race and Media | ||
| Media, Technology, and the Body | ||
| Digital Civil Society | ||
| Advanced Studies in Behavior and Social Media | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Law, Order, & Algorithms | ||
| Networks and Human Behavior | ||
| Media Markets and Social Good | ||
| A.I.-Activism-Art | ||
| Politics of Data: Algorithmic Culture, Big Data, and Information Waste | ||
| Regulating Artificial Intelligence | ||
| Digital Technology and Law: Foundations | ||
| AI and Rule of Law: A Global Perspective | ||
| Language, Gender, & Sexuality | ||
| Networks | ||
| Future of Work: Issues in Organizational Learning and Design | ||
| Data Privacy and Ethics | ||
| Social and Ethical Issues in the Neurosciences | ||
| Universal Basic Income: the philosophy behind the proposal | ||
| Public Opinion and American Democracy | ||
| Money in Politics | ||
| Psychology of Xenophobia | ||
| Intergroup Communication | ||
| Social Networks | ||
| Gender and Technology | ||
| Introduction to Social Networks | ||
| Public Interest Tech: Case Studies | ||
| The Public Life of Science and Technology | ||
| Digital Technology, Society, and Democracy | ||
| Integrative Requirement | 3-5 | |
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| The Psychology of Communication About Politics in America | ||
| Virtual People | ||
| Media Psychology | ||
| Advanced Digital Media Journalism | ||
| Big Local Journalism: a project-based class | ||
| Programming in Journalism | ||
| Building News Applications | ||
| Advanced Studies in Behavior and Social Media | ||
| Language and Technology | ||
| Advanced Topics in Human Virtual Representation | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Exploring Computational Journalism | ||
| Machine Learning with Graphs | ||
| Social Computing | ||
| The Structure of Discourse: Theory and Applications | ||
| Language and Society | ||
| The Politics of Algorithms | ||
| Digital Technology, Society, and Democracy | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Semiotics for Ethnography | ||
| Advanced Statistical Methods for Observational Studies | ||
| Advanced Data Analysis in Qualitative Research | ||
| Introduction to Linguistics | ||
| Social Bias and Earwitness Memory | ||
| Linguistic Meaning and Legal Interpretation | ||
| The Structure of Discourse: Theory and Applications | ||
| Analysis of Variation | ||
| Programming for Linguists | ||
| Spoken Language Processing | ||
| Introduction to Personality and Affective Science | ||
| Introduction to Comparative Studies in Race and Ethnicity | ||
| Psychometrics and automated experiment design | ||
| Data Mining and Analysis | ||
| Introduction to Regression Models and Analysis of Variance | ||
| Total Units | 15-25 | |
Natural Language
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Natural Language. All courses must be taken for 3 units of more. | ||
| Students in the Natural Language Concentration must take four courses from at least 3 of areas 1-7, plus a course from area 8. If any of requirements 1-7 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses (see area 9) must be completed to total 5 courses beyond those that are taken for the Core. | ||
| Mathematical/Computational Foundations | 3-5 | |
| Introduction to the Theory of Computation | ||
| Artificial Intelligence: Principles and Techniques | ||
| Machine Learning | ||
| Modal Logic | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Experimental Methods | ||
| Affective Neuroscience | ||
| Computational Linguistics | 3-5 | |
| From Languages to Information | ||
| Natural Language Processing with Deep Learning | ||
| Spoken Language Processing | ||
| Natural Language Understanding | ||
| Information Retrieval and Web Search | ||
| Natural Language Processing & Text-Based Machine Learning in the Social Sciences | ||
| Challenges for Language Systems | ||
| Phonetics/Phonology/Speech | 3-5 | |
| Phonetics | ||
| Introduction to Phonology | ||
| Seminar in Phonology: Stress, Tone, and Accent | ||
| Sociophonetics | ||
| Advanced Phonetics | ||
| Advanced Phonetics | ||
| Phonology | ||
| Corpus Phonology | ||
| Historical Morphology and Phonology | ||
| Morphosyntax | 3-5 | |
| The Syntax of English | ||
| Crosslinguistic Syntax | ||
| Morphosyntax | ||
| Foundations of Syntactic Theory I | ||
| Seminar in Syntax: Advanced Topics | ||
| Historical Morphosyntax | ||
| Semantics/Pragmatics/Philosophy of Language | 3-5 | |
| Introduction to Semantics and Pragmatics | ||
| Introduction to Lexical Semantics | ||
| Lexical Semantic Typology | ||
| Advanced Semantics | ||
| Advanced Topics in Semantics & Pragmatics | ||
| Lexical Semantics | ||
| Seminar in Semantics: Conditionals | ||
| Wittgenstein | ||
| Philosophy of Language | ||
| Advanced Philosophy of Language | ||
| Naturalizing Representation | ||
| Capstone Seminar: Artificial Intelligence | ||
| Slurs and Derogation: Semantic, Pragmatic and Ethical Perspectives | ||
| Evolution of Signalling | ||
| Advanced Topics in Philosophy of Language | ||
| Challenges for Language Systems | ||
| Psycholinguistics | 3-5 | |
| Learning to Speak: An Introduction to Child Language Acquisition | ||
| Methods in Psycholinguistics | ||
| Foundations of Psycholinguistics | ||
| Seminar in Developmental Psycholinguistics | ||
| Language and Thought | ||
| Introduction to Psycholinguistics | ||
| Neural Network Models of Cognition | ||
| Sociolinguistics and Language Change | 3-5 | |
| African American Vernacular English | ||
| Introduction to Word-Formation | ||
| Language and Society | ||
| Who Speaks Good English | ||
| Sociolinguistics and Pidgin Creole Studies | ||
| Language, Gender, & Sexuality | ||
| Sociophonetics | ||
| American Dialects | ||
| Introduction to Linguistic Typology | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Language and Technology | ||
| Artificial Intelligence: Principles and Techniques | ||
| Information Retrieval and Web Search | ||
| From Languages to Information | ||
| Wittgenstein | ||
| Philosophy of Language | ||
| Advanced Philosophy of Language | ||
| Naturalizing Representation | ||
| Capstone Seminar: Artificial Intelligence | ||
| Slurs and Derogation: Semantic, Pragmatic and Ethical Perspectives | ||
| Evolution of Signalling | ||
| Logic and Artificial Intelligence | ||
| Research Seminar on Logic and Cognition | ||
| Topics in Logic, Information and Agency | ||
| Advanced Topics in Philosophy of Language | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Topics in Natural and Artificial Intelligence | ||
| The Philosophy and Science of Perception | ||
| Conceptual Issues in Cognitive Science | ||
| Contingent Electives | 3-5 | |
| If any of requirements 1-7 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
| Introduction to Statistical Methods: Precalculus | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Neurosciences
See also the Symbolic Systems website.
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Neurosciences. All courses must be taken for 3 units of more. | ||
| Students in the Neurosciences Concentration must take a total of five courses. At least two of the five courses must be from the first two areas, and at least one must come from area 7. If any of the courses listed under areas 1-6 are taken for Core requirements, then additional approved Contingent Elective courses (see area 8) must be completed to total 5 courses beyond those that are taken for the Core. Area 9 (Recommended Add-ons) consists of one- and two-unit courses that supplement areas 1-8. Add-on courses do not count toward the 5-course requirement for the Concentration. | ||
| Basic Neuroscience | 3-5 | |
| Physiology | ||
| Cell Biology | ||
| Human Behavioral Biology | ||
| Mechanisms of Neuron Death | ||
| Cellular Neuroscience: Cell Signaling and Behavior | ||
| Molecular and Cellular Neurobiology | ||
| The Human Organism | ||
| The Nervous System | ||
| Information and Signaling Mechanisms in Neurons and Circuits | ||
| Ion Transport and Intracellular Messengers | ||
| Cognitive Development | ||
| Foundations of Cognition | ||
| Note: NBIO 206 is a 6-unit course, which counts as two concentration courses, from areas 1 and 2. | ||
| Systems Neuroscience | 3-5 | |
| Developmental Neurobiology | ||
| Exploring Neural Circuits | ||
| Educational Neuroscience | ||
| Brain Plasticity | ||
| Introduction to Perception | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Brain Networks | ||
| Advanced Seminar on Memory | ||
| Brain and Decision | ||
| Affective Neuroscience | ||
| Current Debates in Learning and Memory | ||
| Computational Approaches | 3-5 | |
| Systems Biology | ||
| Quantitative Physiology | ||
| Introduction to Robotics | ||
| Machine Learning | ||
| Computational Models of the Neocortex | ||
| Introduction to Neuroelectrical Engineering | ||
| Materials Advances for Neurotechnology: Materials Meet the Mind | ||
| Neuroplasticity and Musical Gaming | ||
| Brain decoding | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Human Neuroimaging Methods | ||
| Computational Neuroimaging | ||
| Neural Network Models of Cognition | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Brain Machine Interfaces: Science, Technology, and Application | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Biological and Computational Approaches to Vision | 3-5 | |
| Computer Vision: Foundations and Applications | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Convolutional Neural Networks for Visual Recognition | ||
| Introduction to Perception | ||
| Image Systems Engineering | ||
| High-level Vision: From Neurons to Deep Neural Networks | ||
| Philosophical and Theoretical Approaches | 3-5 | |
| Social and Ethical Issues in the Neurosciences | ||
| Philosophy of Neuroscience | ||
| Philosophy of Mind | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Theoretical Neuroscience | ||
| Conceptual Issues in Cognitive Science | ||
| Methodological Foundations | 3-5 | |
| Principles and Practice of Optogenetics for Optical Control of Biological Tissues | ||
| Continuous Mathematical Methods with an Emphasis on Machine Learning | ||
| Data Visualization | ||
| Signal Processing and Linear Systems I | ||
| Signal Processing and Linear Systems II | ||
| The Fourier Transform and Its Applications | ||
| Introduction to Linear Dynamical Systems | ||
| Linear Algebra and Matrix Theory | ||
| Introduction to Optimization | ||
| Introduction to Statistical Methods: Precalculus | ||
| Research Methods in Cognition & Development | ||
| Human Neuroimaging Methods | ||
| Experimental Methods | ||
| Statistical Methods for Behavioral and Social Sciences | ||
| Advanced Statistical Modeling | ||
| Statistical Methods in Engineering and the Physical Sciences | ||
| Biostatistics | ||
| Introduction to Applied Statistics | ||
| Introduction to Statistical Inference | ||
| Integrative Requirement | 3-5 | |
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Computer Vision: Foundations and Applications | ||
| Artificial Intelligence: Principles and Techniques | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Deep Learning | ||
| Computer Vision: From 3D Reconstruction to Recognition | ||
| Reinforcement Learning | ||
| Computational Models of the Neocortex | ||
| Philosophy of Neuroscience | ||
| Research Seminar on Logic and Cognition | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Topics in Neurodiversity: Design Thinking Approaches | ||
| Ion Transport and Intracellular Messengers | ||
| Brain Networks | ||
| Brain decoding | ||
| Advanced Seminar on Memory | ||
| Cognitive Neuroscience | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Human Neuroimaging Methods | ||
| Computational Neuroimaging | ||
| Neural Network Models of Cognition | ||
| Brain and Decision | ||
| Theoretical Neuroscience | ||
| Topics in Natural and Artificial Intelligence | ||
| Large-Scale Neural Network Modeling for Neuroscience | ||
| Affective Neuroscience | ||
| Machine Learning Methods for Neural Data Analysis | ||
| Theories of Consciousness | ||
| The Philosophy and Science of Perception | ||
| Conceptual Issues in Cognitive Science | ||
| Cognition in Interaction Design | ||
| Contingent Electives | 3-5 | |
| If any of the courses listed under areas 1-6 are taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Additional courses may be added here in the future. | ||
| Recommended Add-ons | 3-5 | |
| One- and two-unit courses that supplement the offerings above. These courses are recommended, but do not count toward the 5-course requirement for the Concentration: | ||
| NeuroTech Training Seminar | ||
| Experimental Immersion in Neuroscience | ||
| Total Units | 15-25 | |
Philosophical Foundations
See also the Symbolic Systems website.
| Units | ||
|---|---|---|
| Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Philosophical Foundations. All courses must be taken for 3 units of more. | ||
| Philosophy of Mind and Language | 3-5 | |
| One course from the PHIL 180-series: | ||
| Metaphysics | ||
| Realism, Anti-Realism, Irrealism, Quasi-Realism | ||
| Philosophy of Language | ||
| Advanced Philosophy of Language | ||
| Naturalizing Representation | ||
| Truth | ||
| Self-knowledge and Metacognition | ||
| Topics in Epistemology | ||
| Formal Epistemology | ||
| Topics in the Theory of Justification | ||
| Special Topics in Epistemology: Testimony in science and everyday life | ||
| Metaontology | ||
| Philosophy of Mind | ||
| Philosophy of Action | ||
| Paradoxes | ||
| Fine-Tuning Arguments for God's Existence | ||
| Ethics, Historical, and Political Philosophy | 3-5 | |
| Courses must be numbered 100 or above. | ||
| Select one of the following: | ||
| Modern Philosophy, Descartes to Kant | ||
| Plato's Later Metaphysics and Epistemology | ||
| History of Modern Moral Philosophy | ||
| Metaethics | ||
| Philosophy of Law | ||
| Capstone Seminar: The Meaning of Life | ||
| Logic | 3-5 | |
| Select one of the following: | ||
| Introduction to the Theory of Computation | ||
| Computability and Logic | ||
| Modal Logic | ||
| Topics in Logic, Information and Agency | ||
| Philosophy of Science | 3-5 | |
| Select one of the following: | ||
| Philosophy of Artificial Intelligence | ||
| Philosophy of Mathematics | ||
| Central Topics in the Philosophy of Science: Theory and Evidence | ||
| Philosophy of Physics: Space and Time | ||
| Philosophy of Neuroscience | ||
| Evolution of the Social Contract | ||
| Conceptual Issues in Cognitive Science | ||
| Integrative Requirement | ||
| Must be completed no earlier than the Junior Year. | ||
| i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or | ||
| ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options). | ||
| Computers, Ethics, and Public Policy | ||
| Ethics, Public Policy, and Technological Change | ||
| Social and Ethical Issues in the Neurosciences | ||
| Phenomenology: Animals | ||
| Philosophy of Mathematics | ||
| Philosophy of Neuroscience | ||
| Evolution of the Social Contract | ||
| Formal Epistemology | ||
| Capstone Seminar: Artificial Intelligence | ||
| Capstone seminar: Common Sense Philosophy | ||
| What makes a good explanation? Psychological and philosophical perspectives | ||
| Topics in Logic, Information and Agency | ||
| Grad Seminar: Philosophy of Neuroscience | ||
| Topics in Neuroscience | ||
| Topics in Metaphysics and Epistemology: Situations and Attitudes | ||
| Seminar on Emotion | ||
| Theories of Consciousness | ||
| The Philosophy and Science of Perception | ||
| Conceptual Issues in Cognitive Science | ||
| Contingent Electives | ||
| If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core: These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following: | ||
Additional courses may be added here in the future. | ||
| Total Units | 15-25 | |
Individually Designed Concentrations (IDCs)
Individually Designed Concentrations (IDCs) consist of five courses in a coherent subject area related to symbolic systems. This relationship may be established through inclusion in an IDC of two or more courses that connect the proposed concentration to the core, i.e. courses that (a) directly apply disciplines included in the core and (b) are related by topic or methodology to the other courses in the proposed concentration.
Course selection is to be made in consultation with the student's adviser and is subject to approval by the adviser, the Associate Director, and the Director. For examples of IDCs completed by past SSP students, consult the list of alumni and apply the filter "Individually Designed Concentration".
Approval of an IDC must take place no less than two full quarters before a student plans to graduate, e.g. prior to the first day of Winter Quarter of the senior year if a student intends to graduate in June of that year. Failure to obtain approval by the required date will necessitate either completing the requirements for one of the suggested concentrations, or delaying graduation to the end of the second full quarter following approval of an IDC.
To get a proposed IDC approved, send an email message to symsys-directors at lists.stanford.edu, cc'd to your prospective concentration adviser, stating that the adviser has approved your proposal, and giving a title, one-paragraph description, and course plan for your proposed concentration.
Additional Information
Undergraduate Research
The program encourages all SSP majors to gain experience in directed research by participating in faculty research projects or by pursuing independent study. In addition to the Symbolic Systems Honors Program (see below), the following avenues are offered.
Summer Internships: students work on SSP-related faculty research projects. Application procedures are announced in the Winter Quarter for SSP majors.
Research Assistantships: other opportunities to work on faculty research projects are typically announced to SSP majors as they arise during the academic year.
Independent Study: under faculty supervision. For course credit, students should enroll in SYMSYS 196 Independent Study.
Contact SSP for more information on any of these possibilities, or see the Symbolic Systems web site. In addition, see the Undergraduate Advising and Research web site for information on UAR grants and scholarships supporting student research projects at all levels.
Honors Program
Seniors in SSP may apply for admission to the Symbolic Systems honors program prior to the beginning of their final year of study. Students who are accepted into the honors program can graduate with honors by completing an honors thesis under the supervision of a faculty member. Course credit for the honors project may be obtained by registering for SYMSYS 190 Senior Honors Tutorial any quarter while a student is working on an honors project. SYMSYS 191 Senior Honors Seminar, is recommended for honors students during the senior year. Contact SSP or visit the program's web site for more information on the honors program, including deadlines and policies.
Minor in Symbolic Systems
Students may minor in Symbolic Systems by completing either Option 1 or Option 2. For additional information see the Symbolic Systems minors web site.
Degree Requirements
Option 1
| Units | ||
|---|---|---|
| One course in each of the following core areas (please note that several of these courses have prerequisites): | ||
| a. Cognition | 3-4 | |
| Select one of the following: | ||
| Minds and Machines (formerly SYMSYS 100) | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| b. Logic and Computation | 3-5 | |
| Select one of the following: | ||
| Mathematical Logic | ||
| Metalogic | ||
| Mathematical Foundations of Computing | ||
| c. Computer Programming | 3-5 | |
| Select one of the following: | ||
| Programming Abstractions | ||
| Programming Abstractions | ||
| Computer Organization and Systems | ||
| d. Philosophical Foundations | 4-5 | |
| Select one of the following: | ||
| Minds and Machines (formerly SYMSYS 100) | ||
| Mind, Matter, and Meaning | ||
| e. Linguistic Theory | 3-4 | |
| Select one of the following: | ||
| Phonetics | ||
| Introduction to Phonology | ||
| Introduction to Syntax | ||
| The Syntax of English | ||
| Crosslinguistic Syntax | ||
| Introduction to Semantics and Pragmatics | ||
| Introduction to Lexical Semantics | ||
| f. Computation and Cognition | 3-4 | |
| Select one of the following: | ||
| Theoretical Neuroscience | ||
| Artificial Intelligence: Principles and Techniques | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| Computer Vision: Foundations and Applications | ||
| From Languages to Information | ||
LINGUIST 182 | (no longer offered) | |
| Computational Neuroscience | ||
| An introduction to computation and cognition | ||
| Computation and Cognition: The Probabilistic Approach | ||
| Neural Network Models of Cognition | ||
| Total Units | 19 | |
| 1 | SYMSYS 1 Minds and Machines (formerly SYMSYS 100) may not be counted for both areas 'a' and 'd'. |
Option 2
| Introduction | 4 | |
| SYMSYS 1 | Minds and Machines (formerly SYMSYS 100) | 4 |
| Interdisciplinary Concentration | 15 | |
| An interdisciplinary SSP concentration listed on the SSP web site. To qualify, the selection of courses used for the minor must be interdisciplinary; it must either include courses from at least three departments, or include more than one course from each of two departments. | ||
| Total Units | 19 | |
Coterminal Master's Degrees in Symbolic Systems
The Symbolic Systems M.S. Program admits a handful of coterminal students each year. Coterminal students usually complete the program in one academic year.
Applications for Coterminal admission of active Stanford undergraduates are reviewed in the Winter and Spring Quarters. For more details, see the Coterm admissions information on the Symbolic Systems Program website. Admission to the program as a coterminal student is subject to the policies and deadlines described in the "Coterminal Bachelor's and Master's Degrees" section of this bulletin. The GRE is not required for coterminal applicants to the Symbolic Systems M.S. program.
Many SSP majors also complete coterminal M.S. or M.A. degrees in affiliated departments. In addition to the Symbolic Systems M.S. program, the Department of Philosophy offers a Special Program in Symbolic Systems track for interdisciplinary graduate level work leading to the Master of Arts in Philosophy.
University Coterminal Requirements
Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this bulletin. University requirements for the coterminal master’s degree are described in the “Coterminal Master’s Program” section. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin.
After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis.
In this master’s program, courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements.
Course transfers are not possible after the bachelor’s degree has been conferred.
The University requires that the graduate advisor be assigned in the student’s first graduate quarter even though the undergraduate career may still be open. The University also requires that the Master’s Degree Program Proposal be completed by the student and approved by the department by the end of the student’s first graduate quarter.
Master of Science in Symbolic Systems
The University's basic requirements for the M.S. degree are discussed in the "Graduate Degrees" section of this bulletin.
The M.S. degree in Symbolic Systems is designed to be completed in the equivalent of one academic year by coterminal students or returning students who already have a B.S. degree in Symbolic Systems, and in two years or less by other students depending upon level of preparation. Admission is competitive, providing a limited number of students with the opportunity to pursue course and project work in consultation with a faculty adviser who is affiliated with the Symbolic Systems Program. The faculty adviser may impose requirements beyond those described here.
Admission to the program as a coterminal student is subject to the policies and deadlines described in the "Coterminal Bachelor's and Master's Degrees" section of this bulletin. Applicants to the M.S. program are reviewed each Winter Quarter. Information on deadlines, procedures for applying, and degree requirements are available from the program's student services coordinator in the Linguistics Department office (460-127E) and at the Symbolic Systems web site.
Note, the GRE is required for external applicants.
Symbolic Systems also offers a Joint Degree with Law School (M.S./J.D.).
Director of Graduate Studies: Hyowon Gweon
Degree Requirements
A candidate for the M.S. degree in Symbolic Systems must complete a program of 45 units. All courses must be 100-level and above. At least 36 of these must be graded units, passed with an average grade of 3.0 (B) or better, and any course taken as part of the 45 unit program must be taken for a letter grade unless the course is offered S/NC only. None of the 45 units to be counted toward the M.S. degree may include units counted toward an undergraduate degree at Stanford or elsewhere. Course requirements for the M.S. degree in Symbolic Systems may be waived after a review by the program office. Waivers are granted at the discretion of the program, and only if evidence is provided that similar or more advanced courses have been taken and passed with a letter grade of B or its equivalent, either at Stanford or another institution, and as part of another degree program which the student has either completed or is pursuing in parallel with the Symbolic Systems M.S. degree. Course requirements that are waived rather than fulfilled by courses taken at Stanford may not be counted toward the 45 units required for the Symbolic Systems M.S. degree. For additional information, see the Symbolic Systems web site.
Each candidate for the M.S. degree must fulfill the following requirements:
- Submission to the Symbolic Systems Program office and approval of the following pre-project research documents:
- Project Area Statement, endorsed with a commitment from a student's prospective project adviser no later than May 1 of the academic year prior to the expected graduation year; and
- Qualifying Research Paper due no later than the end of the Summer Quarter prior to the expected graduation year.
- Completion of a coherent plan of study, to be approved by the Program Director, Director of Graduate Studies, or Associate Director, in consultation with the student's primary adviser (for students with an approved Project Area Statement), and designed to support a student's project as well as the core course requirements for the M.S. degree (requirements 3 and 4 below). An initial plan of study should be delineated on the Program Proposal Form prior to the end of the student's first quarter of study, as required by the University. The final version of the Program Proposal, which should specify all the courses which the student has taken and proposes in fulfillment of both the Program's and the University's course and unit requirements for the degree, is due by the end of Finals Week in the quarter prior to the student's expected graduation quarter (i.e. end of Winter Quarter for a student graduating in the Spring).
- Completion of the Master's Breadth Requirements. The Program Proposal must include courses taken for 3 units or more each that are more advanced than the Symbolic Systems undergraduate core in four main skill areas: formal, empirical, computational, and philosophical; and in at least three of the following departments (based on the listing as as any cross-listing departments): Computer Science, Linguistics, Philosophy, and Psychology. Courses to fulfill the Breadth Requirements must be taken for a letter grade if available.
Acceptable courses in each of the four required skill areas are defined as follows:
a) Formal: a course in logic and computational theory beyond the level of PHIL 151 Metalogic. The courses below have been approved. Other courses may be approved if appropriate.
- PHIL 252 Computability and Logic
- PHIL 254 Modal Logic
- PHIL 356C Logic and Artificial Intelligence
- PHIL 357 Research Seminar on Logic and Cognition
- CS 154 Introduction to the Theory of Computation
- CS 157 Computational Logic
- CS 161 Design and Analysis of Algorithms
- CS 261 Optimization and Algorithmic Paradigms
b) Empirical: a course drawing on experimental or observational data or methods, beyond the level of PSYCH 55, LINGUIST 120 or 130A. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.
- CS 224N Natural Language Processing with Deep Learning
- CS 224U Natural Language Understanding
- CS 229 Machine Learning
- CS 376 Research Topics in Human-Computer Interaction
- LINGUIST 230B Advanced Semantics
- NBIO 206 The Nervous System
- NBIO 258 Information and Signaling Mechanisms in Neurons and Circuits
- PSYCH 204 Computation and Cognition: The Probabilistic Approach
- PSYCH 204A Human Neuroimaging Methods
- PSYCH 209 Neural Network Models of Cognition
- PSYCH 251 Experimental Methods
- PSYCH 252 Statistical Methods for Behavioral and Social Sciences
- STATS 200 Introduction to Statistical Inference
- SYMSYS 245 Cognition in Interaction Design
c) Computational: a course involving programming beyond the level of CS 107. The courses below have been approved. Other courses may be approved if appropriate.
- CS 108 Object-Oriented Systems Design
- CS 110 Principles of Computer Systems
- CS 124 From Languages to Information
- CS 142 Web Applications
- CS 143 Compilers
- CS 145 Data Management and Data Systems
- CS 148 Introduction to Computer Graphics and Imaging
- CS 210A Software Project Experience with Corporate Partners
- CS 221 Artificial Intelligence: Principles and Techniques
- CS 224N Natural Language Processing with Deep Learning
- CS 224W Machine Learning with Graphs
- CS 246 Mining Massive Data Sets
d) Philosophical: a course in the area of Philosophy of Mind/Language/Science/Epistemology or Metaphysics at the 200 level or above, certified by the instructor as worthy of graduate credit. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.
- PHIL 264 Central Topics in the Philosophy of Science: Theory and Evidence
- PHIL 267D Philosophy of Neuroscience
- PHIL 281 Philosophy of Language
- PHIL 281C
- PHIL 283 Self-knowledge and Metacognition
- PHIL 286 Philosophy of Mind
- PHIL 286A Self-fashioning
- PHIL 287 Philosophy of Action
- PHIL 327 Scientific Philosophy: From Kant to Kuhn and Beyond
- PHIL 348 Evolution of Signalling
- PHIL 359 Topics in Logic, Information and Agency
- PHIL 377 Social Agency
4. Completion of three quarters of SYMSYS 291 Master's Program Seminar.
5. Completion of a substantial project appropriate to the Program Proposal, represented by the M.S. Thesis. The project and thesis normally take three quarters or more to complete, and work on the project may account for up to 15 units of a student's 45-unit program. The thesis must be read and approved for the master's degree in Symbolic Systems by two qualified readers approved by the program, at least one of whom must be a member of the academic council. A hard copy of the thesis must be submitted to the Associate Director of Symbolic Systems, including the signatures of each reader indicating approval of the thesis for the degree of Master of Science, no later than 12 noon on the day of the University Dissertation/Thesis Submission Deadline for the quarter of a student's graduation. A digital copy must be uploaded to the Stanford Digital Repository by the same deadline. For more details, see the Master's Thesis information on the Symbolic Systems Program website.
COVID-19 Policies
On July 30, the Academic Senate adopted grading policies effective for all undergraduate and graduate programs, excepting the professional Graduate School of Business, School of Law, and the School of Medicine M.D. Program. For a complete list of those and other academic policies relating to the pandemic, see the "COVID-19 and Academic Continuity" section of this bulletin.
The Senate decided that all undergraduate and graduate courses offered for a letter grade must also offer students the option of taking the course for a “credit” or “no credit” grade and recommended that deans, departments, and programs consider adopting local policies to count courses taken for a “credit” or “satisfactory” grade toward the fulfillment of degree-program requirements and/or alter program requirements as appropriate.
Undergraduate Degree Requirements
Grading
Other Policies
Graduate Degree Requirements
Grading
The master's program in Symbolic Systems counts all courses taken in academic year 2020-21 with a grade of 'D-', 'CR' (credit) or 'S' (satisfactory) towards satisfaction of graduate degree requirements that otherwise require a letter grade, subject to a graduate GPA requirement of 3.0 or above in the courses that constitute a master's student's 45 required units.Graduate Advising Expectations
The Symbolic Systems Program is committed to providing academic advising in support of graduate student scholarly and professional development. When most effective, this advising relationship entails collaborative and sustained engagement by both the adviser and the advisee. As a best practice, advising expectations should be periodically discussed and reviewed to ensure mutual understanding. Both the adviser and the advisee are expected to maintain professionalism and integrity.
Faculty advisers guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways.
Graduate students are active contributors to the advising relationship, proactively seeking academic and professional guidance and taking responsibility for informing themselves of policies and degree requirements for their graduate program. Students are expected to meet regularly with their advisers and to keep them informed about their academic progress. Each student and their adviser should mutually agree on the frequency of these meetings when the advising relation begins and reassess their frequency at the start of every quarter.
For a statement of University policy on graduate advising, see the "Graduate Advising" section of this bulletin.
Faculty
Director: Michael C. Frank
Director of Graduate Studies: Hyowon Gweon
Associate Director: Todd Davies
Faculty Advisory Board: Jeremy Bailenson, Michael Bernstein, Ray Briggs, Todd Davies, Judith Degen, Michael C. Frank, Noah Goodman, Hyowon Gweon, Thomas Icard, Daniel Jurafsky, Daniel Lassiter, Krista Lawlor, Christopher Manning, James McClelland, Stanley Peters, Christopher Potts, Mehran Sahami, Johan van Benthem, Thomas A. Wasow
Executive Committee: Michael Bernstein, Todd Davies, Michael C. Frank, Hyowon Gweon, Thomas Icard, Christopher Potts
Program Faculty:
Aeronautics and Astronautics: Mykel Kochenderfer (Assistant Professor)
Biology: Deborah Gordon (Professor)
Classics: Reviel Netz (Professor)
Communication: Jeremy Bailenson (Professor), Jeff Hancock (Professor), Byron Reeves (Professor), Frederick Turner (Professor)
Computer Science: Maneesh Agrawala (Professor), Michael Bernstein (Assistant Professor), Emma Brunskill (Assistant Professor), David Dill (Professor, emeritus), Chelsea Finn (Assistant Professor), Michael Genesereth (Associate Professor), Oussama Khatib (Professor), Daphne Koller (Adjunct Professor), James Landay (Professor), Jean-Claude Latombe (Professor, emeritus), Marc Levoy (Professor, emeritus), Christopher Manning (Professor), Andrew Ng (Adjunct Professor), Chris Piech (Assistant Professor), Vaughan Pratt (Professor, emeritus), Eric Roberts (Professor, emeritus), Mehran Sahami (Professor, Teaching), Yoav Shoham (Professor, emeritus), Terry Winograd (Professor, emeritus)
Economics: Muriel Niederle (Professor)
Education: Nick Haber (Assistant Professor), Raymond P. McDermott (Professor, emeritus), Roy Pea (Professor), Daniel Schwartz (Professor)
Electrical Engineering: Chelsea Finn (Assistant Professor), Krishna Shenoy (Professor), Sebastian Thrun (Adjunct Professor)
French and Italian: Jean-Pierre Dupuy (Professor)
Genetics: Russ B. Altman (Professor)
Graduate School of Business: Baba Shiv (Professor)
History: Jessica G. Riskin (Professor)
Law: Mark Lemley (Professor)
Linguistics: Arto Anttila (Associate Professor), Joan Bresnan (Professor, emerita), Eve Clark (Professor, emerita), Cleo Condoravdi (Professor Research), Judith Degen (Assistant Professor), Penelope Eckert (Professor), Vera Gribanova (Associate Professor), Boris Harizanov (Assistant Professor), Daniel Jurafsky (Professor), Ronald Kaplan (Adjunct Professor), Lauri Karttunen (Adjunct Professor), Martin Kay (Professor), Paul Kiparsky (Professor), Daniel Lassiter (Assistant Professor), Beth Levin (Professor), Christopher Manning (Professor), Stanley Peters (Professor, emeritus), Christopher Potts (Professor), Meghan Sumner (Associate Professor), Thomas A. Wasow (Professor, emeritus), Annie Zaenen (Adjunct Professor)
Management Science and Engineering: Sharad Goel (Assistant Professor), Pamela Hinds (Professor), John Ugander (Assistant Professor)
Mathematics: Persi Diaconis (Professor)
Mechanical Engineering: Sean Follmer (Assistant Professor)
Medicine: Russ B. Altman (Professor), Mark Musen (Professor)
Music: Jonathan Berger (Professor), Christopher Chafe (Professor), Eleanor Selfridge-Field (Adjunct Professor), Ge Wang (Associate Professor)
Neurobiology: William T. Newsome (Professor), Jennifer Raymond (Professor)
Philosophy: Michael Bratman (Professor), Ray Briggs (Professor), Rosa Cao (Assistant Professor), Mark Crimmins (Associate Professor), John Etchemendy (Professor), Dagfinn Føllesdal (Professor, emeritus), Thomas Icard III (Assistant Professor), Krista Lawlor (Professor), Anna-Sara Malmgren (Assistant Professor), John Perry (Professor, emeritus), Brian Skyrms (Professor), Johan van Benthem (Professor), Thomas A. Wasow (Professor, emeritus)
Psychiatry and Behavioral Sciences: Vinod Menon (Professor)
Psychology: Herbert H. Clark (Professor, emeritus), Anne Fernald (Associate Professor), Michael C. Frank (Associate Professor), Justin Gardner (Assistant Professor), Noah Goodman (Associate Professor), Kalanit Grill-Spector (Professor), Hyowon Gweon (Assistant Professor), Brian Knutson (Professor), Ellen Markman (Professor), James McClelland (Professor), Russell Poldrack (Professor), Barbara Tversky (Professor, emerita), Anthony Wagner (Professor), Brian Wandell (Professor), Daniel Yamins (Assistant Professor), Jamil Zaki (Assistant Professor)
Statistics: Persi Diaconis (Professor), Susan P. Holmes (Professor)
Symbolic Systems: Todd Davies (Associate Director), Jeff Shrager (Adjunct Professor), Paul Skokowski (Adjunct Professor)
Other Affiliates: David Barker-Plummer (CSLI Engineering Research Associate), Keith Devlin H-STAR Operation Senior Researcher), Daniel Flickinger (CSLI Research and Development Engineer), Cheryl Phillips (Lecturer in Communications)
Courses
SYMSYS 1. Minds and Machines. 4 Units.
(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Same as: CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 200
SYMSYS 1P. A Practical Introduction to Symbolic Systems. 2 Units.
An optional supplement to "Minds and Machines" (SYMSYS 1), aimed at prospective majors in Symbolic Systems. Students will learn from the perspectives of faculty, alums, and advanced students about how to navigate the many paths available to a student: Sym Sys versus other majors, undergraduate core options, selecting courses and a concentration, research opportunities, internships, the honors program, graduate programs, careers, and life paths.
SYMSYS 2S. Introduction to Cognitive Science. 3 Units.
Cognitive Science explores one of sciences final frontiers; the scientific study of the human mind. It is a broad interdisciplinary field that encompasses research from areas in neuroscience, psychology, philosophy, linguistics, and computer science and covers topics such as the nature of knowledge, thinking, remembering, vision, imagery, language, and consciousness. All of which we will touch upon in this survey course and is intended to give students a sampler of each discipline. This introductory class will expose students to some of the major methodologies, experimental design, neuroscientific fundamentals, and different cognitive disorders. More importantly, it will help students refine their interest to a specific field within cognitive science for future studies at their respective institutions. This 6-week summer course will require a sizable amount of required reading, not all of the readings is covered in the lectures. To extend and complement topics in this field, there is material presented in the lectures that is not in the readings.
SYMSYS 8. The Logic Group. 1-2 Unit.
If all dogs bark and Fido is a dog, it follows that Fido barks. If Clark Kent owns a car, it follows that Superman owns a car, since Clark Kent is Superman. Yet you might wonder why these statements follow from the said assumptions. Can this perhaps be explained in terms of the statements¿ meanings or their grammatical form? Will the explanation be the same in both cases, or do statements follow from assumptions for a variety of different reasons? Are there laws or principles which conclusively prove the statements from the assumptions? Can these laws be doubted, or are they self-evident?nThe Logic Group will tackle these and similar questions. You will gain a solid understanding of both propositional and predicate logic, including a deductive proof system. You will familiarise yourself with the central concepts of formal reasoning, including syntax and semantics, truth and interpretation, validity and soundness, and the concept of logical consequence. Although formal and technical, the course is accessible to all students, and all may benefit. Studying logic will improve your analytic and critical thinking skills and help you develop a more rigorous and precise writing style. Only open to students residing at Stanford House in Oxford (UK).
Same as: Oxford
SYMSYS 20Q. The Data-Driven World. 3 Units.
Recent technological advancements have enabled us to measure, record, and analyze more data than ever before. How can we effectively use this data to solve real-world problems and better understand the world around us? In this course, we will learn how computers can create a statistical model to learn from human-generated data and find patterns or make predictions. We will explore different algorithms that create a wide variety of models, each with their own pros and cons. Through R programming exercises integrated across the course, we will apply these models to many different kinds of data sourced from urban development, education, business, etc. and analyze our findings. Based on individual interest, students will choose to investigate a specific research question using domain-specific data as part of a quarter-long project. Lastly, we will discuss important ethical debates on the possible uses of data and their implications in today¿s world. By the end of the course, students will develop a technical coding skillset to investigate hypotheses in any given dataset, and be able to connect the insights they derive to larger issues of society, equity, and justice.
SYMSYS 112. Challenges for Language Systems. 3-4 Units.
Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.
Same as: SYMSYS 212
SYMSYS 115. Critique of Technology. 3-4 Units.
What is the character of technology? How does technology reveal aspects of human nature and social practices? How does it shape human experience and values? We will survey the history of philosophy of technology -- from ancient and enlightenment ideas, to positivist and phenomenological conceptions -- to develop a deeper understanding of diverse technological worldviews. This will prepare us to consider contemporary questions about the "ethos" of technology. Specific questions will vary depending upon the interests of participants, but may include: ethical and existential challenges posed by artificial intelligence; responsible product design in the "attention economy"; industry regulation and policy issues for information privacy; and the like. PREREQUISITES: PHIL80.
SYMSYS 122. Artificial Intelligence: Philosophy, Ethics, & Impact. 3-4 Units.
Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of this course is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
SYMSYS 167D. Philosophy of Neuroscience. 4 Units.
How can we explain the mind? With approaches ranging from computational models to cellular-level characterizations of neural responses to the characterization of behavior, neuroscience aims to explain how we see, think, decide, and even feel. While these approaches have been highly successful in answering some kinds of questions, they have resulted in surprisingly little progress in others. We'll look at the relationships between the neuroscientific enterprise, philosophical investigations of the nature of the mind, and our everyday experiences as creatures with minds. Prerequisite: PHIL 80.n(Not open to freshmen.).
Same as: PHIL 167D, PHIL 267D
SYMSYS 168A. A.I.-Activism-Art. 3-5 Units.
Lecture/studio course exploring arts and humanities scholarship and practice engaging with, and generated by, emerging emerging and exponential technologies. Our course will explore intersections of art and artificial intelligence with an emphasis on social impact and racial justice. Open to all undergraduates.
Same as: ARTHIST 168A, CSRE 106A, ENGLISH 106A
SYMSYS 190. Senior Honors Tutorial. 1-5 Unit.
Under the supervision of their faculty honors adviser, students work on their senior honors project. May be repeated for credit.
SYMSYS 191. Senior Honors Seminar. 1 Unit.
Recommended for seniors doing an honors project. Under the leadership of the Symbolic Systems program coordinator, students discuss, and present their honors project.
SYMSYS 192. Symbolic Systems in Practice. 3 Units.
A professionalization course that fulfills the Practicum requirement of the Symbolic Systems undergraduate major Capstone. Online lectures, readings, assigned exercises, and live discussions relate the Sym Sys curriculum to a substantial work experience. Must be accompanied by an approved internship totaling 64 hours or more of total work time, which must be completed in the quarter prior to, during, or immediately following the course.
SYMSYS 195A. Design for Artificial Intelligence. 3-4 Units.
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking.
Same as: CS 247A
SYMSYS 195B. Design for Behavior Change. 3-4 Units.
Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS147 or equivalent.
Same as: CS 247B
SYMSYS 195D. Research in Digital Democracy. 3-4 Units.
Digital democracy refers to social activity that is organized democratically at a group, institutional, or societal level, and that takes place within or is augmented by digital technology. This is a project-based research seminar designed to teach students methods for studying digital democracy, as well as collaborating in a group, the organization of a research project, and academic writing. The first few weeks of the course will be an overview of digital democracy research and its methods, as well as a time for students to organize into a group research project, The remainder of the class (about 7 weeks) will be spent performing and writing up the research for a targeted publication venue. Application required for enrollment. Prerequisite: At least one course in empirical methods or statistics. Prerequisites: At least one course in empirical methods or statistics. Contact the instructor at davies@stanford.edu to apply to enroll in this course.
Same as: SYMSYS 295D
SYMSYS 195E. Experimental Methods. 3 Units.
Graduate laboratory class in experimental methods for psychology, with a focus on open science methods and best practices in behavioral research. Topics include experimental design, data collection, data management, data analysis, and the ethical conduct of research. The final project of the course is a replication experiment in which students collect new data following the procedures of a published paper. The course is designed for incoming graduate students in psychology, but is open to qualified students from other programs who have some working knowledge of the R statistical programming language. Requirement: PSYCH 10/STATS 60 or equivalent.
Same as: PSYCH 251
SYMSYS 195G. Introduction to Game Design. 3-4 Units.
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; please plan on attending every studio to take this class. nThe focus of CS247g is an introduction to theory and practice of the design of games. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. This class has multiple short projects, allowing us to cover a variety of genres, from narrative to pure strategy. Prerequisites: 147 or equivalent background.
Same as: CS 247G
SYMSYS 195I. Image Systems Engineering. 1-3 Unit.
This course is an introduction to digital imaging technologies. We focus on the principles of key elements of digital systems components; we show how to use simulation to predict how these components will work together in a complete image system simulation. The early lectures introduce the software environment and describe options for the course project. The following topics are covered and software tools are introduced:n- Basic principles of optics (Snell's Law, diffraction, adaptive optics).n- Image sensor and pixel designsn- Color science, metrics, and calibrationn- Human spatial resolutionn- Image processing principlesn- Display technologiesnA special theme of this course is that it explains how imaging technologies accommodate the requirements of the human visual system. The course also explains how image systems simulations can be useful in neuroscience and industrial vision applications.nThe course consists of lectures, software tutorials, and a course project. Tutorials and projects include extensive software simulations of the imaging pipeline. Some background in mathematics (linear algebra) and programming (Matlab) is valuable.nPre-requisite: EE 261 or equivalent. Or permission of instructor required.
Same as: PSYCH 221
SYMSYS 195L. Methods in Psycholinguistics. 4 Units.
Over the past ten years, linguists have become increasingly interested in testing theories with a wider range of empirical data than the traditionally accepted introspective judgments of hand-selected linguistic examples. Consequently, linguistics has seen a surge of interest in psycholinguistic methods across all subfields. This course will provide an overview of various standard psycholinguistic techniques and measures, including offline judgments (e.g., binary categorization tasks like truth-value judgments, Likert scale ratings, continuous slider ratings), response times, reading times, eye-tracking, ERPs, and corpus methods. Students will present and discuss research articles. Students will also run an experiment (either a replication or an original design, if conducive to the student¿s research) to gain hands-on experience with experimental design and implementation in html/javascript and Mechanical Turk; data management, analysis, and visualization in R; and open science tools like git/github.
Same as: LINGUIST 245B
SYMSYS 195N. Natural Language Processing with Deep Learning. 3-4 Units.
Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Same as: CS 224N, LINGUIST 284
SYMSYS 195S. Service Design. 3-4 Units.
A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247S is Service Design. In this course we will be looking at experiences that address the needs of multiple types of stakeholders at different touchpoints - digital, physical, and everything in between. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. Let us explore what specialized tools and processes are required to created these multi-faceted interactions. Prerequisites: CS147 or equivalent background in design thinking.
Same as: CS 247S
SYMSYS 195T. Natural Language Processing & Text-Based Machine Learning in the Social Sciences. 4 Units.
Digital communications (including social media) are the largest data sets of our time, and most of it is text. Social scientists need to be able to digest small and big data sets alike, process it and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical analysis of language variables in R. Familiarity with Python is helpful but not required. Basic familiarity with R is expected. The ability to wrangle data into a spreadsheet-like format is expected. A basic introduction to SQL will be given in the course. Familiarity with SSH and basic Linux is helpful but not required. Understanding of regression is expected.
Same as: PSYCH 290, SOC 281
SYMSYS 195U. Natural Language Understanding. 3-4 Units.
Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, or CS 224S.
Same as: CS 224U, LINGUIST 188, LINGUIST 288
SYMSYS 195V. Data Visualization. 3-4 Units.
Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of CS147, CS148, or equivalent.
Same as: CS 448B
SYMSYS 196. Independent Study. 1-15 Unit.
Independent work under the supervision of a faculty member. Can be repeated for credit.
SYMSYS 200. Minds and Machines. 4 Units.
(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Same as: CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1
SYMSYS 201. Digital Technology, Society, and Democracy. 3 Units.
The impact of information and communication technologies on social and political life. Interdisciplinary. Classic and contemporary readings focusing on topics such as social networks, virtual versus face-to-face communication, the public sphere, voting technology, and collaborative production. Prerequisite: Completion of a course in psychology, communication, human-computer interaction, or a related discipline, or consent of the instructor.
SYMSYS 202. Theories of Consciousness. 3 Units.
Are fish conscious? Are fetuses? Could we build a conscious computer? Much of the philosophical work on consciousness has focused on whether consciousness is wholly physical, but that question is orthogonal to the more specific questions about consciousness that most of us really care about. To answer those questions, we need a theory of how consciousness works in our world. Philosophers and scientists have put forward a spectrum of different candidates, from very abstract, philosophical theories through theories more informed by cognitive psychology down to neural and even quantum theories. In this seminar, students will learn about the major theories of consciousness as well as conceptual issues that arise on different approaches. Particularly important will be the question of how we might gain empirical evidence for a theory of consciousness.
SYMSYS 203. Cognitive Science Perspectives on Humanity and Well-Being. 3 Units.
In recent years, cognitive scientists have turned more attention to questions that have traditionally been investigated bynhistorians, political scientists, sociologists, and anthropologists, e.g. What are the sources of conflict and disagreement betweennpeople?, What drives or reduces violence and injustice?, and What brings about or is conducive to peace and justice? In this advancednsmall seminar, we will read and discuss works by psychologists, neuroscientists, philosophers, and others, which characterize thisngrowing research area among those who study minds, brains, and behavior.nRequired: Completion of a course in psychology beyond the level of PSYCH 1, or consent of the instructor.
SYMSYS 205. The Philosophy and Science of Perception. 3 Units.
Our senses tell us about our immediate environment, but what exactly do they tell us? Our color experiences tell us that the things around us have color properties, but what in the world are color properties? Do we visually represent absolute size as well as relative size? When we see an apple, do we literally see it as an apple, or do we infer that it¿s an apple based on its color and shape? Can what we expect to see affect what we actually see? In this seminar we will bring both philosophical and empirical perspectives to bear on these and other issues related to figuring out just how our perceptual experiences represent the world as being. Prerequisite: PHIL 80 or permission of the instructor.
SYMSYS 207. Conceptual Issues in Cognitive Science. 3 Units.
This seminar will cover a selection of foundational issues in cognitive science. Topics may include modularity, representation, connectionism, neuroscience and free will, neuroimaging, implants, sensory experience, the nature of information, and consciousness. Course is limited to 15 students. Prerequisite: PHIL 80, or permission of the instructor.
SYMSYS 208. Computer Machines and Intelligence. 3 Units.
It has become common for us to see in the media news about computer winning a masters in chess, or answering questions on the Jeopardy TV show, or the impact of AI on health, transportation, education, in the labor market and even as an existential threat to mankind. This interest in AI gives rise questions such as: Is it possible for a computer to think? What is thought? Are we computers? Could machines feel emotions or be conscious? Curiously, there is no single, universally accepted definition of Artificial Intelligence. However in view of the rapid dissemination of AI these questions are important not only for experts, but also for all other members of society. This course is intended for students from different majors Interested in learn how the concept of intelligent machine is understood by the researchers in AI. We will study the evolution of AI research, its different approaches, with focus on the tests developed to verify if a machine is intelligent or not. In addition, we will examine the philosophical problems associated with the concept of intelligent machine. The topics covered will include: Turing test, symbolic AI, connectionist AI, sub- symbolic Ai, Strong AI and Weak AI, Ai singularity, unconventional computing, rationality, intentionality, representation, machine learning, and the possibility of conscious machines.
SYMSYS 212. Challenges for Language Systems. 3-4 Units.
Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.
Same as: SYMSYS 112
SYMSYS 245. Cognition in Interaction Design. 3 Units.
Note: Same course as 145 which is no longer active. Interactive systems from the standpoint of human cognition. Topics include skill acquisition, complex learning, reasoning, language, perception, methods in usability testing, special computational techniques such as intelligent and adaptive interfaces, and design for people with cognitive disabilities. Students conduct analyses of real world problems of their own choosing and redesign/analyze a project of an interactive system. Limited enrollment seminar taught in two sections of approximately ten students each. Admission to the course is by application to the instructor, with preference given to Symbolic Systems students of advanced standing. Recommended: a course in cognitive psychology or cognitive anthropology.
SYMSYS 255. Building Digital History: Informatics of Social Movements and Protest. 3-5 Units.
A participatory course focused on the online representation of oral and archival history research. This year's thematic focus is the design and evaluation of history websites focused on social movements and protest. We will survey the field of digital history and its application to social movement research and teaching. The course will utilize materials developed in the 2014 version of the course, which focused on the history of student activism at Stanford. Class will apply lessons from digital history practice and theory to the design of an online repository and community for the collaborative representation and discussion of social movement history at Stanford, and to the further development of source material in a future version of the class. Topics will include participatory design, studies of historical learning, archiving issues, data integrity, and fair representation of different viewpoints, among others.
SYMSYS 255A. Building Digital History: Social Movements and Protest at Stanford. 1 Unit.
Lectures-only version of SYMSYS 255.
SYMSYS 271. Group Democracy. 2-4 Units.
This seminar will explore theoretical, empirical, and practical approaches to groups that come together around a common purpose or interest. Emphasis is on democratically structured, non-hierarchical and non-institutional decision making, e.g. by grassroots activists, student, or neighborhood organizations. Parliamentary, consensus, and informal procedures. How do groups form? How do they deliberate and make decision? What are the principles underlying different models for group process, and how well do different procedures work in practice? How do culture and identity affect the working of a group? And how are social technologies used? Readings from different disciplines and perspectives. Course is limited to 20 students. Prerequisite: A course in social psychology, decision making or group sociology. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit.
SYMSYS 275. Collective Behavior and Distributed Intelligence. 3 Units.
This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.
Same as: BIO 175
SYMSYS 280. Symbolic Systems Research Seminar. 1 Unit.
A mixture of public lectures of interest to Symbolic Systems students (the Symbolic Systems Forum) and student-led meetings to discuss research in Symbolic Systems. Can be repeated for credit. Open to both undergraduates and Master's students.
SYMSYS 290. Master's Degree Project. 1-15 Unit.
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SYMSYS 291. Master's Program Seminar. 1 Unit.
Enrollment limited to students in the Symbolic Systems M.S. degree program. May be repeated for credit.
SYMSYS 295D. Research in Digital Democracy. 3-4 Units.
Digital democracy refers to social activity that is organized democratically at a group, institutional, or societal level, and that takes place within or is augmented by digital technology. This is a project-based research seminar designed to teach students methods for studying digital democracy, as well as collaborating in a group, the organization of a research project, and academic writing. The first few weeks of the course will be an overview of digital democracy research and its methods, as well as a time for students to organize into a group research project, The remainder of the class (about 7 weeks) will be spent performing and writing up the research for a targeted publication venue. Application required for enrollment. Prerequisite: At least one course in empirical methods or statistics. Prerequisites: At least one course in empirical methods or statistics. Contact the instructor at davies@stanford.edu to apply to enroll in this course.
Same as: SYMSYS 195D
SYMSYS 296. Independent Study. 1-15 Unit.
Independent work under the supervision of a faculty member. Can be repeated for credit.
SYMSYS 297. Teaching in Symbolic Systems. 1-5 Unit.
Leading sections, grading, and/or other duties of teaching or helping to teach a course in Symbolic Systems. Sign up with the instructor supervising the course in which you are teaching or assisting.
SYMSYS 298. Peer Advising in Symbolic Systems: Practicum. 1-2 Unit.
Optional for students selected as Undergraduate Advising Fellows in the Symbolic Systems Program. AFs work with program administrators to assist undergraduates in the Symbolic Systems major or minor, in course selection, degree planning, and relating the curriculum to a career or life plan, through advising and events. Meeting with all AFs for an hour once per week under the direction of the Associate Director. Requires a short reflective paper at the end of the quarter on what the AF has learned about advising students in the program. Repeatable for credit. May not be taken by students who receive monetary compensation for their work as an AF.
SYMSYS 299. Curricular Practical Training. 1 Unit.
Students obtain employment in a relevant research or industrial activity to enhance their professional experience consistent with their degree programs. Meets the requirements for curricular practical training for students on F-1 visas. Students submit a concise report detailing work activities, problems worked on, and key results. May be repeated for credit. Prerequisite: qualified offer of employment and consent of advisor.
