Mail Code: 94305-2150
Phone: (650) 723-4284
Email: symsys-afs@lists.stanford.edu
Web Site: http://symsys.stanford.edu
Courses offered by the Program in Symbolic Systems 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 leading to a B.S. in Symbolic Systems provides students with a core of concepts and techniques, drawing on faculty and courses from various departments. The curriculum prepares students for advanced training in the interdisciplinary study of language and information, or for postgraduate study in any of the main contributing disciplines. It is also excellent preparation for employment immediately after graduation.
Symbolic Systems majors must complete a core of required courses plus a field of study consisting of five additional courses. All major courses are to be taken for letter grades unless an approved course is offered satisfactory/no credit only. All core courses must be passed with a grade of 'C-' or better. Students who receive a grade lower than this in a core course must alert the program of this fact so that a decision can be made about whether the student should continue in the major.
Core Requirements
In order to graduate with a B.S. in Symbolic Systems, a student must complete the following requirements. Some of these courses have other courses as prerequisites; students are responsible for completing each course's prerequisites before they take it. With the exception of the advanced small seminar requirement, courses cannot be used towards more than one area of the core requirements. For additional information, see the Symbolic Systems web site. Note: Students matriculating in the Class of 2018 or later must take SYMSYS 100 Minds and Machines before their declaration of the Symbolic Systems undergraduate major can be approved.
1. Introductory Core Course
Students matriculating in the Class of 2018 or later must take SYMSYS 100 Minds and Machines before their declaration of the Symbolic Systems undergraduate major can be approved.
| Units | ||
|---|---|---|
| SYMSYS 100 | Minds and Machines | 4 |
2. Continuous Fundamentals Level 1—Single Variable Calculus
| Units | ||
|---|---|---|
| Select one of the following Series: | ||
| Series A | 10 | |
10 units of Advanced Placement Calculus credit | ||
| Series B | 10 | |
| Calculus and Calculus and Calculus | ||
| Series C | 10 | |
| Calculus | ||
or MATH 41A | Calculus ACE | |
| Calculus | ||
or MATH 42A | Calculus ACE | |
| Series D | ||
Equivalent preparation in Single Variable Calculus, as judged by student | ||
3. Continuous Fundamentals Level 2—Multivariable Calculus
| Units | ||
|---|---|---|
| Select one of the following: 1 | ||
| CME 100 | Vector Calculus for Engineers | 5 |
| CME 100A | Vector Calculus for Engineers, ACE | 6 |
| MATH 51 | Linear Algebra and Differential Calculus of Several Variables | 5 |
| MATH 51A | Linear Algebra and Differential Calculus of Several Variables, ACE | 6 |
| MATH 51H | Honors Multivariable Mathematics | 5 |
| 1 | MATH 52 Integral Calculus of Several Variables and/or MATH 53 Ordinary Differential Equations with Linear Algebra, or CME 102 Ordinary Differential Equations for Engineers and/or CME 104 Linear Algebra and Partial Differential Equations for Engineers, are recommended and may be required for some optional higher level courses. |
4. Continuous Fundamentals Level 3—Probability and Statistics
| Units | ||
|---|---|---|
| Select one of the following: | ||
| CS 109 | Introduction to Probability for Computer Scientists | 3-5 |
| STATS 110 | Statistical Methods in Engineering and the Physical Sciences | 4-5 |
| STATS 116 | Theory of Probability | 3-5 |
| MSE 120 | Probabilistic Analysis | 5 |
| EE 178 | Probabilistic Systems Analysis | 4 |
| MATH 151 | Introduction to Probability Theory | 3 |
| CME 106/ENGR 155C | Introduction to Probability and Statistics for Engineers | 3-4 |
5. Discrete Fundamentals
| Units | ||
|---|---|---|
| a. Computing Level 1 | 3-5 | |
| Programming Methodology | ||
Or equivalent preparation, as judged by student | ||
| b. Computing Level 2 | 3-5 | |
| Select one of the following: | ||
| Programming Abstractions | ||
| Programming Abstractions (Accelerated) | ||
| c. Logic and Computational Theory | 3-5 | |
| Select one of the following: | ||
| Mathematical Foundations of Computing | ||
| Mathematical Logic | ||
| Logic in Action: A New Introduction to Logic | ||
6. Technical Depth
Two courses chosen from the list below (from either the same or different areas), appropriate to a student’s concentration. Students concentrating in HCI, AI, or Computer Music must take CS 107 Computer Organization and Systems. Other concentrations may also restrict the particular courses that can be taken to fulfill this requirement. See concentration lists at http://symsys.stanford.edu/viewing/htmldocument/16190
| Units | ||
|---|---|---|
| Area A. Computer Programming | ||
| CS 107 | Computer Organization and Systems (required for HCI, AI, or Computer Music) | 3-5 |
| Area B. Computational Theory 1 | ||
| CS 154 | Introduction to Automata and Complexity Theory | 3-4 |
| CS 161 | Design and Analysis of Algorithms | 3-5 |
| PHIL 151A | Recursion Theory | 4 |
| Area C. Logic | ||
| CS 157 | Logic and Automated Reasoning | 3 |
| PHIL 151 | Metalogic | 4 |
| PHIL 152 | Computability and Logic | 4 |
| PHIL 154 | Modal Logic | 4 |
| Area D. Decision Theory/Game Theory | ||
| CS 224M | Multi-Agent Systems | 3 |
| ECON 160 | Game Theory and Economic Applications | 5 |
| ECON 180 | Honors Game Theory | 5 |
| MSE 252 | Decision Analysis I: Foundations of Decision Analysis | 3-4 |
| Area E. Probability and Statistics | ||
| STATS 200 | Introduction to Statistical Inference | 3 |
| CS 228 | Probabilistic Graphical Models: Principles and Techniques | 3-4 |
| 1 | CS 156 is not offered in 2015-16 but may be used to fulfill this requirement. |
7. Philosophical Foundations Level 1
| Units | ||
|---|---|---|
| Introductory Philosophy 1 | 3-5 | |
| Select one of the following: | ||
| Introduction to Philosophy | ||
| Introduction to Moral Philosophy | ||
| Introduction to Philosophy of Science | ||
| Modern Philosophy, Descartes to Kant | ||
| Existentialism | ||
| Evil | ||
| All three of the following SLE (must complete all three): | ||
| Structured Liberal Education | ||
| Structured Liberal Education | ||
| Structured Liberal Education | ||
| Other introductory courses taught in the Philosophy Department, if approved by the Program Director or Associate Director | ||
| 1 | SLE 91, 92, 93 (Must complete entire sequence). |
8. Philosophical Foundations Level 2
| Units | ||
|---|---|---|
| PHIL 80 | Mind, Matter, and Meaning | 5 |
9. Philosophical Foundations Level 3
| Units | ||
|---|---|---|
| Select one of the following advanced undergraduate course in metaphysics/epistemology(post-PHIL 80): 1 | ||
| Plato's Metaphysics and Epistemology | ||
| Metaethics | ||
| Philosophy of Law | ||
| Metaphysics | ||
| Realism, Anti-Realism, Irrealism, Quasi-Realism | ||
| Philosophy of Language | ||
| Truth | ||
| Epistemology | ||
| Philosophy of Mind | ||
| Philosophy of Action | ||
Note: Symbolic Systems majors must take PHIL 182 for 3 or more units.
10. Cognition and Neuroscience
| Units | ||
|---|---|---|
| PSYCH 45 | Introduction to Learning and Memory | 3 |
| PSYCH 50 | Introduction to Cognitive Neuroscience | 4 |
| An additional undergraduate course in cognition and/or neurosciences 1 | ||
| Select one of the following: | ||
| Introduction to Brain and Behavior | ||
| Human Behavioral Biology | ||
| Introduction to Perception | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| Introduction to Developmental Psychology | ||
| Introduction to Social Psychology | ||
| Language and Thought | ||
| Cognitive Development | ||
| Judgment and Decision-Making | ||
11. Natural Language
| Units | ||
|---|---|---|
| Language and Mind | 4 | |
| Select one of the following: | ||
| Introduction to Linguistics | ||
| Introduction to Speech Perception | ||
| Language Acquisition I | ||
| Language and Thought | ||
| Linguistic Theory | 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 | ||
| Syntactic Theory and Implementation | ||
12. Computation and Cognition
| Units | ||
|---|---|---|
| A course applying core technical skills to cognition | ||
| Select one of the following: | ||
| Artificial Intelligence: Principles and Techniques | ||
| Multi-Agent Systems | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| From Languages to Information | ||
| Computational Theories of Syntax | ||
| Computation and cognition: the probabilistic approach | ||
| Neural network and deep learning models for cognition and cognitive neuroscience | ||
| Formal and Computational Approaches in Psychology and Cognitive Science | ||
Advanced Small Seminar Requirement
An upper-division, limited-enrollment seminar drawing on material from other courses in the core. Courses listed under Symbolic Systems Program offerings with numbers from SYMSYS 200 through SYMSYS 209 are acceptable, as are other courses found in the course list below (other courses may be added throughout the Autumn Quarter). Total enrollment must not exceed 20 students for a course to be approved as fulfilling the Advanced Small Seminar Requirement. A course taken to fulfill this requirement can also be counted toward another requirement, as part of either the core or a student's concentration, but not both.
| Units | ||
|---|---|---|
| LINGUIST 236 | Seminar in Semantics: Modality and Conditionals | 4 |
| MUSIC 220C | Research Seminar in Computer-Generated Music | 2-4 |
| PHIL 194D | Capstone Seminar: Analyticity | 4 |
| PHIL 348 | Evolution of Signalling | 2-4 |
| PHIL 359 | Topics in Logic, Information and Agency | 2-4 |
| PHIL 385D | Topics in Philosophy of Language | 2-4 |
| PSYCH 145 | Seminar on Infant Development | 1-2 |
| PSYCH 169 | Advanced Seminar on Memory | 3 |
| PSYCH 232 | Brain and Decision Making | 3 |
| PSYCH 251 | Affective Neuroscience | 3 |
| SYMSYS 201 | ICT, Society, and Democracy | 3 |
| SYMSYS 206 | Philosophy of Neuroscience | 4 |
| SYMSYS 245 | Cognition in Interaction Design | 3 |
Fields of Study
In addition to the core requirements listed above, the Symbolic Systems major requires each student to complete a field of study consisting of five courses that are thematically related to each other. Students select concentrations from the list below or design others in consultation with their advisers. The field of study is declared on Axess; it appears on the transcript but not on the diploma.
- Applied Logic
- Artificial Intelligence
- Cognitive Science
- Computer Music
- Decision Making and Rationality
- Human-Computer Interaction
- Learning
- Natural Language
- Neurosciences
- Philosophical Foundations
Note: A course may not count toward both a core and a concentration requirement, unless it is applied to the Advanced Small Seminar area within the core. A course that is applied to the Advanced Small Seminar requirement may also be counted toward a student's concentration or toward another core requirement, if appropriate, but not to both.
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 Associated 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.
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, the Undergraduate Advising and Research office offers grants and scholarships supporting student research projects at all levels; see http://ual.stanford.edu/OO/research_opps/Grants.
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.
Option 1
One course in each of the following core areas (please note that several of these courses have prerequisites):
| Units | ||
|---|---|---|
| a. Cognition | ||
| Select one of the following: | ||
| Minds and Machines 1 | ||
| Introduction to Learning and Memory | ||
| Introduction to Cognitive Neuroscience | ||
| b. Logic and Computation | ||
| Select one of the following: | 3-5 | |
| Mathematical Logic | ||
| Logic in Action: A New Introduction to Logic | ||
| Metalogic | ||
| Mathematical Foundations of Computing | ||
| c. Computer Programming | ||
| Select one of the following: | 3-5 | |
| Programming Abstractions | ||
| Programming Abstractions (Accelerated) | ||
| Computer Organization and Systems | ||
| d. Philosophical Foundations | ||
| Select one of the following: | ||
| Minds and Machines 1 | ||
| Mind, Matter, and Meaning | ||
| e. Linguistic Theory | ||
| 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 | ||
| Syntactic Theory and Implementation | ||
| f. Computation and Cognition | 3-4 | |
| Select one of the following: | ||
| Artificial Intelligence: Principles and Techniques | ||
| Multi-Agent Systems | ||
| Probabilistic Graphical Models: Principles and Techniques | ||
| Machine Learning | ||
| From Languages to Information | ||
| Computational Theories of Syntax | ||
| Computation and cognition: the probabilistic approach | ||
| Neural network and deep learning models for cognition and cognitive neuroscience | ||
| Formal and Computational Approaches in Psychology and Cognitive Science | ||
| 1 | SYMSYS 100 Minds and Machines may not be counted for both areas 'a' and 'd'.
|
Option 2
SYMSYS 100 Minds and Machines, plus 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.
Coterminal Master's Degrees in Symbolic Systems
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 three quarters prior to the first graduate quarter, or later, are eligible for consideration for transfer to the graduate career. 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 adviser 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 is 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.
Degree Requirements
A candidate for the M.S. degree in Symbolic Systems must complete a program of 45 units.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 are waived only if evidence is provided that similar or more advanced courses have been taken, either at Stanford or another institution. Courses that are waived rather than taken may not be counted toward the 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 Graduate Studies Director in consultation with the student's adviser and designed to support a student's project. 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, to be modified at the time of the Project Area Statement with the approval of a student's adviser and the Graduate Studies Director. The final version of the Program Proposal, which should specify all the courses the student has taken and proposes as fulfillment of the 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). The plan of study 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: Computer Science, Linguistics, Philosophy, and Psychology. More advanced courses in each of the 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 350A Model Theory
- PHIL 355 Logic and Social Choice
- CS 154 Introduction to Automata and Complexity Theory
- CS 157 Logic and Automated Reasoning
- CS 161 Design and Analysis of Algorithms
- CS 364A Algorithmic Game Theory
b) empirical: a course drawing on experimental or observational data or methods, beyond the level of Psych 55, Ling 120, or Ling 130A. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.
- COMM 268. Experimental Research in Advanced User Interfaces (same as COMM 168, COMM 368, ME 468)
- COMM 269. Computers and Interfaces: Psychological and Social Issues (same as COMM 169)
- CS 224N Natural Language Processing
- CS 376 Human-Computer Interaction Research
- LINGUIST 230B Semantics and Pragmatics I
- LINGUIST 241 Language Acquisition II
- LINGUIST 274C Linguistic Field Methods: Syntax
- 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 and Deep Learning Models for Cognition and Cognitive Science
- PSYCH 252 Statistical Methods for Behavioral and Social Sciences
- PSYCH 254 Lab in Experimental Methods
- 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 148 Introduction to Computer Graphics and Imaging
- CS 221 Artificial Intelligence: Principles and Techniques
- CS 224N Natural Language Processing
- CS 224W Social Information and Network Analysis
- CS 249A Object-Oriented Programming from a Modeling and Simulation Perspective
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 267B Philosophy, Biology, and Behavior
- PHIL 280 Metaphysics
- PHIL 281 Philosophy of Language
- PHIL 285B Philosophy of Perception
- PHIL 286 Philosophy of Mind
- PHIL 287 Philosophy of Action
- PHIL 383B What's an Inference?
- SYMSYS 206 Philosophy of Neuroscience
3. Completion of three quarters of SYMSYS 291 Master's Program Seminar.
4. Completion of a substantial project appropriate to the program plan, represented by the M.S. Thesis, the last of the the M.S research documents. The project normally takes three quarters, and work on the project may account for up to 15 units of a student's 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 copy of the thesis must be submitted (in both print and electronic forms) to the Associate Director of Symbolic Systems, with the print version 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.
Director: Thomas A. Wasow
Director of Graduate Studies: Thomas A. Wasow
Associate Director: Todd Davies
Program Committee Michael Bernstein, Herbert Clark, Todd Davies, Michael C. Frank, Daniel Jurafsky, Krista Lawlor, Christopher Manning, James McClelland, Stanley Peters, Christopher Potts, Eric Roberts, Kenneth A. Taylor, Johan van Benthem, Thomas A. Wasow, Daniel Lassiter, Thomas Icard
Program Faculty:
Applied Physics: Bernardo Huberman (Consulting Professor)
Art and Art History: Scott Bukatman (Professor)
Classics: Reviel Netz (Professor)
Civil and Environmental Engineering: John Kunz (Program Consultant)
Communication: Jeremy Bailenson (Associate Professor), Byron Reeves (Professor), Frederick Turner (Associate Professor)
Computer Science: Michael Bernstein (Assistant Professor), David Dill (Professor), Michael Genesereth (Associate Professor), Oussama Khatib (Professor), Daphne Koller (Consulting Professor), James Landay (Professor), Jean-Claude Latombe (Professor, emeritus), Marc Levoy (Professor, emeritus), Christopher Manning (Professor), Andrew Ng (Associate Professor), Nils Nilsson (Professor, emeritus), Vaughan Pratt (Professor, emeritus), Eric Roberts (Professor), Tim Roughgarden (Associate Professor), Mehran Sahami (Professor, Teaching), Yoav Shoham (Professor), Sebastian Thrun (Professor, Research), Terry Winograd (Professor, emeritus)
Economics: Muriel Niederle (Professor)
Education: BJ Fogg (Consulting Professor), Raymond P. McDermott (Professor), Roy Pea (Professor), Daniel Schwartz (Professor)
Electrical Engineering: Krishna Shenoy (Professor)
French and Italian: Jean-Pierre Dupuy (Professor)
Genetics: Russ B. Altman (Professor)
Graduate School of Business: Baba Shiv (Professor)
History: Jessica G. Riskin (Associate Professor)
Linguistics: Arto Anttila (Associate Professor), Joan Bresnan (Professor, emerita), Eve Clark (Professor), Cleo Condoravdi (Professor Research), Penelope Eckert (Professor), Daniel Jurafsky (Professor), Ronald Kaplan (Consulting Professor), Lauri Karttunen (Consulting Professor), Martin Kay (Professor), Daniel Lassiter (Assistant Professor), Beth Levin (Professor), Christopher Manning (Professor), Stanley Peters (Professor, emeritus), Christopher Potts (Associate Professor), Meghan Sumner (Associate Professor), Thomas A. Wasow (Professor, emeritus), Annie Zaenen (Consulting Professor),
Management Science and Engineering: Pamela Hinds (Associate Professor)
Mathematics: Persi Diaconis (Professor), Solomon Feferman (Professor, emeritus)
Medicine: Russ B. Altman (Professor), Mark Musen (Professor)
Music: Jonathan Berger (Professor), Christopher Chafe (Professor), Eleanor Selfridge-Field (Consulting Professor), Ge Wang (Assistant Professor)
Neurobiology: Ben Barres (Professor), William T. Newsome (Professor), Jennifer Raymond (Associate Professor)
Philosophy: Michael Bratman (Professor), Alexis Burgess (Assistant Professor), Mark Crimmins (Associate Professor), John Etchemendy (Professor), Solomon Feferman (Professor, emeritus), Dagfinn Føllesdal (Professor, emeritus), Thomas Icard III (Assistant Professor), Krista Lawlor (Associate Professor), Anna-Sara Malmgren (Assistant Professor), John Perry (Professor, emeritus), Brian Skyrms (Professor), Kenneth Taylor (Professor), Johan van Benthem (Professor), Thomas A. Wasow (Professor, emeritus)
Psychiatry and Behavioral Sciences: Vinod Menon (Professor, Research)
Psychology: Herbert H. Clark (Professor), Anne Fernald (Associate Professor), Michael C. Frank (Assistant Professor), Noah Goodman (Assistant Professor), Kalanit Grill-Spector (Associate Professor), Hyowon Gweon (Assistant Professor), Brian Knutson (Associate Professor), Ellen Markman (Professor), James McClelland (Professor), Samuel McClure (Assistant Professor), Barbara Tversky (Professor, emerita), Anthony Wagner (Professor), Brian Wandell (Professor)
Statistics: Persi Diaconis (Professor), Susan P. Holmes (Professor)
Symbolic Systems: Todd Davies (Lecturer), Jeff Shrager (Consulting Professor), Paul Skokowski (Consulting Professor)
Other Affiliates: David Barker-Plummer (CSLI Engineering Research Associate), Keith Devlin H-STAR Operation Senior Researcher), Daniel Flickinger (CSLI Research and Development Engineer)
Courses
SYMSYS 100. Minds and Machines. 4 Units.
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. Undergraduates considering a major in symbolic systems should take this course as early as possible in their program of study.
Same as: LINGUIST 144, PHIL 99, PSYCH 35
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 130. Research Methods in the Cognitive and Information Sciences. 3 Units.
Understanding the different methodological approaches used in disciplines that study cognition and information. Emphasis is on philosophical/analytical, formal/mathematical, empirical, and computational thinking styles, with some attention to other methods as well. What assumptions underlie these methods? How can they be combined? How do practitioners of each discipline think differently about problems, and what are the challenges involved in studying or working across them?.
SYMSYS 150. CRYPTOCURRENCIES SEMINAR. 2 Units.
A weekly seminar allowing students the opportunity to discuss and explore cryptocurrencies from a variety of domains and view points:nn1) Explore the history of fiat currencies, both economically and philosophically. How does Bitcoin mesh in here? What are advantages and disadvantages compared to traditional fiat currencies? (~2 weeks) n2) Contextualize and juxtapose decentralized currencies with respect to TCP/IP, Napster, and other relevant decentralized and cloud protocols. (~2 weeks)n3) Work through and understand Satoshi¿s initial protocol and proof-of-work mining system. What problem did she solve? How? Why was it important? How can we prove it mathematically? What are significant game theoretic and cryptographic weaknesses? What do alternative cryptocurrencies look like? Is there a `best¿ alternative? (~3 weeks)n4) What does ¿Bitcoin as a protocol¿ mean? What can be built on top of it? What¿s being built around it? What does regulation look like? What are hypotheses for the future of digital currencies? How do we explain investor confidence, given regulatory hesitation? (~3 weeks).
SYMSYS 161. Applied Symbolic Systems in Venture Capital + Entrepreneurship. 2 Units.
A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.
Same as: SYMSYS 261
SYMSYS 170. Decision Behavior: Theory and Evidence. 3-4 Units.
Introduction to the study of judgment and decision making, relating theory and evidence from disciplines such as psychology, economics, statistics, neuroscience, and philosophy. The development and critique of Homo economicus as a model of human behavior, and more recent theories based on empirical findings. Recommended: background in formal reasoning.
Same as: SYMSYS 270
SYMSYS 184. Syntactic Theory and Implementation. 4 Units.
Analysis and implementation of grammatical phenomena of English. Introduction to a theory of formal grammar, and its computational realization. Practical experience in forming linguistic hypotheses and testing them via implementation using state-of-the-art language technology.
Same as: LINGUIST 184
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 196. Independent Study. 1-15 Unit.
Independent work under the supervision of a faculty member. Can be repeated for credit.
SYMSYS 200. Symbolic Systems in Practice. 2-3 Units.
Applying a Symbolic Systems education at Stanford and outside. The basics of research and practice. Students develop and present a project, and investigate different career paths, including academic, industrial, professional, and public service, through interviews with alumni.
SYMSYS 201. ICT, 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 203. Cognitive Science Perspectives on Conflict, Violence, Peace, and Justice. 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 204. Philosophy of Linguistics. 4 Units.
Philosophical issues raised by contemporary work in linguistics. Topics include: the subject matter of linguistics (especially internalism vs. externalism), methodology and data (especially the role of quantitative methods and the reliance on intuitions), the relationship between language and thought (varieties of Whorfianism and anti-Whorfianism), nativist arguments about language acquisition, and language evolution.
Same as: LINGUIST 204, PHIL 369
SYMSYS 206. Philosophy of Neuroscience. 4 Units.
Can problems of mind be solved by understanding the brain, or models of the brain? The views of philosophers and neuroscientists who believe so, and others who are skeptical of neurophilosophical approaches to the mind. Historical and recent literature in philosophy and neuroscience. Topics may include perception, memory, neural accounts of consciousness, neurophenomenology, neuroscience and physics, computational models, and eliminativism. (Not open to freshmen.).
Same as: PHIL 167D, PHIL 267D
SYMSYS 209. Battles Over Bits. 3 Units.
The changing nature of information in the Internet age and its relationship to human behavior. Philosophical assumptions underlying practices such as open source software development, file sharing, common carriage, and community wireless networks, contrasted with arguments for protecting private and commercial interests such as software patents, copy protection, copyright infringement lawsuits, and regulatory barriers. Theory and evidence from disciplines including psychology, economics, computer science, law, and political science. Prerequisite: PSYCH 40, 55, 70, or SYMBSYS 202.
SYMSYS 210. Learning Facial Emotions: Art and Psychology. 3 Units.
Artistic and psychological learning approaches for emotion recognition from facial expressions. The advantages of learning by image-based microexpressions, subtle expressions, macro expressions, art drawing and actor mimicry when there are cognitive deficits due to conditions such as autism. Comparative analysis uses brain studies, learning theory, and human-computer interaction. Studio component conveys the artistic and psychological approaches. Prerequisites: PSYCH 1, SYMSYS 100 or consent of instructor. Go to www.stanford.edu/~dwilkins/Symsys210Enroll.doc to sign up for a Permission Number.
SYMSYS 211. Learning Facial Emotions: Art, Psychology, Human-Computer Interaction. 3 Units.
Learning to recognize facial emotions by drawing a live model versus the psychology method of using classified images of subtle and micro expressions. Dimensions of analysis include cognitive modeling and neuroscience. The design of human-computer interaction systems for people with cognitive deficits such as autism and Aspergers, which integrate the art and psychology approaches using methods such as robot heads, avatars, and facial recognition software. Prerequisites: PSYCH 1 or consent of instructor.
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: Social Movements and Protest at Stanford. 3-5 Units.
A project-based course focused on developing a collaborative history website based on oral and archival history research. Thematic focus is the history of student activism at Stanford. How have political activities such as demonstrations, assemblies, educational events, and nonviolent civil disobedience been organized on campus, and how have they affected Stanford? What lessons can be drawn from the past for students interested in social change? Students will choose historical periods and/or specific social movements for research. Course will feature guest appearances by representatives from a range of social movements at Stanford the past fifty years, and the building of an online repository and community for the collaborative representation and discussion of history.
SYMSYS 255A. Building Digital History: Social Movements and Protest at Stanford. 1 Unit.
Lectures-only version of SYMSYS 255.
SYMSYS 261. Applied Symbolic Systems in Venture Capital + Entrepreneurship. 2 Units.
A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.
Same as: SYMSYS 161
SYMSYS 270. Decision Behavior: Theory and Evidence. 3-4 Units.
Introduction to the study of judgment and decision making, relating theory and evidence from disciplines such as psychology, economics, statistics, neuroscience, and philosophy. The development and critique of Homo economicus as a model of human behavior, and more recent theories based on empirical findings. Recommended: background in formal reasoning.
Same as: SYMSYS 170
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 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.
