SYMSYS 17SI: Evaluating Education Technology
This seminar assesses the impact of education technologies on learners, teachers, and education systems. Through weekly case studies of ed tech ventures, students will experience and evaluate popular education technologies such as VR, personalized learning, makerspaces, and MOOCs. Additionally, students will develop a toolkit of concepts including critical pedagogy, constructivism, behaviorism, and social reconstructionism which they can use to assess education technologies and their personal contributions to the field. This course will focus largely on ventures in the U.S., but the frameworks developed will be applicable to equity and access issues in education throughout the world. Apply here at https://goo.gl/forms/vv3k0BXvioH2Etby2 by March 30th at 5 pm.
Terms: Spr

Units: 12

Grading: Satisfactory/No Credit
Instructors:
Wolf, J. (PI)
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)
...
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Instructors:
Berger, J. (PI)
;
Bernstein, M. (PI)
;
Clark, E. (PI)
;
Davies, T. (PI)
;
Frank, M. (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)
;
Shiv, B. (PI)
;
Shrager, J. (PI)
;
Sumner, M. (PI)
;
Wagner, A. (PI)
;
Wilkins, D. (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)
;
Davies, T. (PI)
...
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Instructors:
BarkerPlummer, D. (PI)
;
Boroditsky, L. (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 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
Instructors:
Paulo Guimaraes De Assis, L. (PI)
SYMSYS 275: Collective Behavior and Distributed Intelligence (BIO 175)
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.
Terms: not given this year

Units: 3

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)
...
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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)
;
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)
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