printer friendly pagePHYSICS 112: Mathematical Methods of Physics
Theory of complex variables, complex functions, and complex analysis. Fourier series and Fourier transforms. Special functions such as Laguerre, Legendre, and Hermite polynomials, and Bessel functions. The uses of Green's functions. Covers material of
MATH 106 and
MATH 132 most pertinent to Physics majors. Prerequisites: MATH 50 or 50H series, and
MATH 131P or
MATH 173.
Terms: Win
| Units: 4
| UG Reqs: GER: DB-NatSci, WAY-FR
Instructors:
Raghu, S. (PI)
;
Maharaj, A. (TA)
PHYSICS 113: Computational Physics
Numerical methods for solving problems in mechanics, electromagnetism, quantum mechanics, and statistical mechanics. Methods include numerical integration; solutions of ordinary and partial differential equations; solutions of the diffusion equation, Laplace's equation and Poisson's equation with relaxation methods; statistical methods including Monte Carlo techniques; matrix methods and eigenvalue problems. Short introduction to MatLab, used for class examples; class projects may be programmed in any language such as C. Prerequisites:
MATH 53 and PHYS 120. Previous programming experience not required.
Terms: Aut
| Units: 4
| UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-FR
Instructors:
Abel, T. (PI)
;
Nosarzewski, B. (TA)
PHYSICS 120: Intermediate Electricity and Magnetism I
(First in a two-part series: PHYS 120, PHYS 121.) Vector analysis. Electrostatic fields, including boundary-value problems and multipole expansion. Dielectrics, static and variable magnetic fields, magnetic materials. Maxwell's equations. Prerequisites:
PHYSICS 43 or PHYS 63;
MATH 52 and
MATH 53. Pre- or corequisite:
MATH 131P or
MATH 173. Recommended corequisite: PHYS 112.
Terms: Win
| Units: 4
| UG Reqs: GER: DB-NatSci, WAY-FR, WAY-SMA
PHYSICS 130: Quantum Mechanics
The origins of quantum mechanics and wave mechanics. Schrödinger equation and solutions for one-dimensional systems. Commutation relations. Generalized uncertainty principle. Time-energy uncertainty principle. Separation of variables and solutions for three-dimensional 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. Pre- or corequisites:
PHYSICS 120 and
MATH 131P or
MATH 173.
Terms: Win
| Units: 4
| UG Reqs: GER: DB-NatSci, WAY-FR, WAY-SMA
POLISCI 152: Introduction to Game Theoretic Methods in Political Science (POLISCI 352)
Concepts and tools of non-cooperative 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.
Last offered: Spring 2014
| UG Reqs: WAY-FR, WAY-SI
POLISCI 153: Strategy: An Introduction to Game Theory
This course provides an introduction to basic concepts in game theory and strategic reasoning. We discuss ideas such as commitment, credibility, adverse selection, signaling and reputation. Concepts are developed through games played in class, and applied to politics, economics, business and everyday life.
Terms: Aut
| Units: 5
| UG Reqs: WAY-FR
| Repeatable
2 times
(up to 10 units total)
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, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
PSYCH 35: Minds and Machines (LINGUIST 144, PHIL 99, 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:DB-SocSci, WAY-FR
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 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: Aut
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR
Instructors:
Sabatti, C. (PI)
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, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Bacallado, S. (PI)
;
DiCiccio, C. (PI)
;
Mukherjee, R. (PI)
...
more instructors for STATS 60 »
Instructors:
Bacallado, S. (PI)
;
DiCiccio, C. (PI)
;
Mukherjee, R. (PI)
;
Sun, D. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
DiCiccio, C. (TA)
;
Janson, L. (TA)
;
Sun, D. (TA)
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