PHYSICS 130: Quantum Mechanics I
The origins of quantum mechanics and wave mechanics. Schrödinger equation and solutions for onedimensional systems. Commutation relations. Generalized uncertainty principle. Timeenergy uncertainty principle. Separation of variables and solutions for threedimensional systems; application to hydrogen atom. Spherically symmetric potentials and angular momentum eigenstates. Spin angular momentum. Addition of angular momentum. Prerequisites:
PHYSICS 65 or
PHYSICS 70 and
PHYSICS 111 or
MATH 131P or
MATH 173.
MATH 173 can be taken concurrently. Pre or corequisites:
PHYSICS 120.
Terms: Win

Units: 4

UG Reqs: GER: DBNatSci, WAYFR, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Burchat, P. (PI)
POLISCI 152: Introduction to Game Theoretic Methods in Political Science (POLISCI 352)
Concepts and tools of noncooperative game theory developed using political science questions and applications. Formal treatment of Hobbes' theory of the state and major criticisms of it; examples from international politics. Primarily for graduate students; undergraduates admitted with consent of instructor.
Terms: not given this year

Units: 35

UG Reqs: WAYFR, WAYSI

Grading: Letter or Credit/No Credit
POLISCI 153: Thinking Strategically (POLISCI 354)
This course provides an introduction to strategic reasoning. We discuss ideas such as the commitment problem, credibility in signaling, cheap talk, moral hazard and adverse selection. Concepts are developed through games played in class, and applied to politics, business and everyday life.
Terms: Win

Units: 5

UG Reqs: WAYFR

Repeatable for credit

Grading: Letter (ABCD/NP)
Instructors:
Acharya, A. (PI)
POLISCI 153Z: Thinking Strategically
This course provides an introduction to strategic reasoning. We discuss ideas such as the commitment problem, credibility in signaling, cheap talk, moral hazard and adverse selection. Concepts are developed through games played in class, and applied to politics, business and everyday life.
Terms: Sum

Units: 4

UG Reqs: WAYFR

Grading: Letter (ABCD/NP)
Instructors:
Acharya, A. (PI)
PSYCH 10: Introduction to Statistical Methods: Precalculus (STATS 60, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for PSYCH 10 »
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
PSYCH 35: Minds and Machines (LINGUIST 35, PHIL 99, SYMSYS 1)
(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. Undergraduates considering a major in symbolic systems should take this course as early as possible in their program of study.
Terms: Aut

Units: 4

UG Reqs: GER:DBSocSci, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Skokowski, P. (PI)
STATS 48N: Riding the Data Wave
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To an
more »
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Terms: not given this year

Units: 3

UG Reqs: WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum

Units: 45

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
;
Zhang, Y. (PI)
;
Cauchois, M. (TA)
...
more instructors for STATS 116 »
Instructors:
Donoho, D. (PI)
;
Zhang, Y. (PI)
;
Cauchois, M. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (TA)
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