SYMSYS 161: Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future (SYMSYS 261)
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.
Terms: Spr

Units: 2

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Dar, Z. (PI)
;
Li, N. (PI)
SYMSYS 190: Senior Honors Tutorial
Under the supervision of their faculty honors adviser, students work on their senior honors project. May be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 15

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Berger, J. (PI)
;
Bernstein, M. (PI)
;
Clark, E. (PI)
...
more instructors for SYMSYS 190 »
Instructors:
Berger, J. (PI)
;
Bernstein, M. (PI)
;
Clark, E. (PI)
;
Davies, T. (PI)
;
Frank, M. (PI)
;
Gardner, J. (PI)
;
GrillSpector, K. (PI)
;
Gross, J. (PI)
;
Icard, T. (PI)
;
Jurafsky, D. (PI)
;
Klemmer, S. (PI)
;
Knutson, B. (PI)
;
Lassiter, D. (PI)
;
Lobell, D. (PI)
;
McClelland, J. (PI)
;
McClure, S. (PI)
;
Nass, C. (PI)
;
Potts, C. (PI)
;
Raymond, J. (PI)
;
Shiv, B. (PI)
;
Shrager, J. (PI)
;
Sumner, M. (PI)
;
Wagner, A. (PI)
;
Wilkins, D. (PI)
;
Zaki, J. (PI)
SYMSYS 196: Independent Study
Independent work under the supervision of a faculty member. Can be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
BarkerPlummer, D. (PI)
;
Boroditsky, L. (PI)
;
Crum, A. (PI)
...
more instructors for SYMSYS 196 »
Instructors:
BarkerPlummer, D. (PI)
;
Boroditsky, L. (PI)
;
Crum, A. (PI)
;
Davies, T. (PI)
;
Fernald, R. (PI)
;
Fogg, B. (PI)
;
Foster, G. (PI)
;
Frank, M. (PI)
;
Gross, J. (PI)
;
Icard, T. (PI)
;
Jurafsky, D. (PI)
;
Karttunen, L. (PI)
;
Kay, M. (PI)
;
Klemmer, S. (PI)
;
Knutson, B. (PI)
;
McClelland, J. (PI)
;
McClure, S. (PI)
;
Menon, V. (PI)
;
Mints, G. (PI)
;
Nass, C. (PI)
;
Sahami, M. (PI)
;
Shelef, O. (PI)
;
Shenoy, K. (PI)
;
Shiv, B. (PI)
;
Shrager, J. (PI)
;
Sumner, M. (PI)
;
Wasow, T. (PI)
;
Wilkins, D. (PI)
;
Zaki, J. (PI)
SYMSYS 203: Cognitive Science Perspectives on Humanity and WellBeing
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.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Davies, T. (PI)
SYMSYS 207: Conceptual Issues in Cognitive Science
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.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Skokowski, P. (PI)
SYMSYS 261: Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future (SYMSYS 161)
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.
Terms: Spr

Units: 2

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Dar, Z. (PI)
;
Li, N. (PI)
SYMSYS 265: Quantum Algorithms and Quantum Cognition
Quantum computers can solve some classes of problems with more efficiency than classical computers, usually exponentially faster. They have the potential to solve in minutes problems that would take for a classical computer longer than the age of the universe. Among the promising applications are the development of new drugs, and new materials, machine learning and cryptographic key breaking, just to mention a few examples. Until recently the idea of building a computer seemed like a project reserved for a distant future, but over the past years many companies such as IBM, Google, Microsoft, DWave, Rigetti Computing, and others have announced that they started the operation of quantum computer prototypes. However, due to the counterintuitive properties of quantum theory the creation of quantum algorithms has been as difficult as hardware development. Although there are many algorithms built to run on quantum computers there are very few that use the full potential of quantum computing
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Quantum computers can solve some classes of problems with more efficiency than classical computers, usually exponentially faster. They have the potential to solve in minutes problems that would take for a classical computer longer than the age of the universe. Among the promising applications are the development of new drugs, and new materials, machine learning and cryptographic key breaking, just to mention a few examples. Until recently the idea of building a computer seemed like a project reserved for a distant future, but over the past years many companies such as IBM, Google, Microsoft, DWave, Rigetti Computing, and others have announced that they started the operation of quantum computer prototypes. However, due to the counterintuitive properties of quantum theory the creation of quantum algorithms has been as difficult as hardware development. Although there are many algorithms built to run on quantum computers there are very few that use the full potential of quantum computing. The purpose of this course is to teach the fundamentals of quantum computing and quantum algorithms for students with nonphysics background. The emphasis of the course will be to develop a "quantum intuition" by presenting the main differences between classical and quantum logic, as well as the use of special examples developed in quantum cognition. Quantum cognition applies the mathematical formalism of quantum mechanics in psychology and decision theories in situations where conventional formalism does not work. The topics covered will include: the basics of quantum theory and quantum computation, Classical and Quantum Logic, Classical and Quantum gates, Quantum Cognition, the main Quantum algorithms such as Phil's Algorithm, Deutsch Algorithm, DeutschJozsa Algorithm, Simon's algorithm, Shor's Algorithm, and Grover's Algorithm. This course has workshop format involving readings followed by short lectures, discussion, plus other activities in class, homework, and Final Project. Required background: linear algebra, calculus equivalent to
MATH 19 and
MATH 20, basic probability theory and complex numbers. Students are not expected to have taken previous courses in quantum mechanics.
Terms: Spr

Units: 4

Grading: Letter or Credit/No Credit
SYMSYS 280: Symbolic Systems Research Seminar
A mixture of public lectures of interest to Symbolic Systems students (the Symbolic Systems Forum) and studentled meetings to discuss research in Symbolic Systems. Can be repeated for credit. Open to both undergraduates and Master's students.nFirst meeting is the second Monday of the quarter
Terms: Aut, Win, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Davies, T. (PI)
SYMSYS 290: Master's Degree Project
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Anttila, A. (PI)
;
Bailenson, J. (PI)
;
BarkerPlummer, D. (PI)
...
more instructors for SYMSYS 290 »
Instructors:
Anttila, A. (PI)
;
Bailenson, J. (PI)
;
BarkerPlummer, D. (PI)
;
Bernstein, M. (PI)
;
Bidadanure, J. (PI)
;
Boroditsky, L. (PI)
;
Cain, J. (PI)
;
Davies, T. (PI)
;
Degen, J. (PI)
;
Frank, M. (PI)
;
Goodman, N. (PI)
;
Gross, J. (PI)
;
Gweon, H. (PI)
;
Icard, T. (PI)
;
Jurafsky, D. (PI)
;
Kay, M. (PI)
;
Klemmer, S. (PI)
;
Knutson, B. (PI)
;
Levin, B. (PI)
;
Manning, C. (PI)
;
McClelland, J. (PI)
;
McClure, S. (PI)
;
Nass, C. (PI)
;
Potts, C. (PI)
;
Reeves, B. (PI)
;
Sahami, M. (PI)
;
Shiv, B. (PI)
;
Shrager, J. (PI)
;
Sumner, M. (PI)
;
Taylor, K. (PI)
;
Thille, C. (PI)
;
Wagner, A. (PI)
;
Wilkins, D. (PI)
;
Zaki, J. (PI)
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