PHIL 152: Computability and Logic (PHIL 252)
Approaches to effective computation: recursive functions, register machines, and Turing machines. Proof of their equivalence, discussion of Church's thesis. Elementary recursion theory. These techniques used to prove Gödel's incompleteness theorem for arithmetic, whose technical and philosophical repercussions are surveyed. Prerequisite: 151.
Terms: Spr

Units: 4

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
Instructors:
Icard, T. (PI)
;
Mierzewski, C. (TA)
PHIL 154: Modal Logic (PHIL 254)
(Graduate students register for 254.) Syntax and semantics of modal logic and its basic theory: including expressive power, axiomatic completeness, correspondence, and complexity. Applications to topics in philosophy, computer science, mathematics, linguistics, and game theory. Prerequisite: 150 or preferably 151.
Terms: Spr

Units: 4

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
van Benthem, J. (PI)
;
Zaffora Blando, F. (TA)
PHIL 162: Philosophy of Mathematics (MATH 162, PHIL 262)
(Graduate students register for
PHIL 262.) General survey of the philosophy of mathematics, focusing on epistemological issues. Includes survey of some basic concepts (proof, axiom, definition, number, set); mindbending theorems about the limits of our current mathematical knowledge, such as Gödel's Incompleteness Theorems, and the independence of the continuum hypothesis from the current axioms of set theory; major philosophical accounts of mathematics: Logicism, Intuitionism, Hilbert's program, Quine's empiricism, Field's program, Structuralism; concluding with a discussion of Eugene Wigner's `The Unreasonable Effectiveness of Mathematics in the Natural Sciences'. Students won't be expected to prove theorems or complete mathematical exercises. However, includes some material of a technical nature. Prerequisite: PHIL150 or consent of instructor.
Terms: Aut

Units: 4

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
PHIL 166: Probability: Ten Great Ideas About Chance (PHIL 266, STATS 167, STATS 267)
Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of
STATS 60 or 116.
Terms: not given this year

Units: 4

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
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:
Baiocchi, M. (PI)
;
DiCiccio, C. (PI)
;
Janson, L. (PI)
...
more instructors for PSYCH 10 »
Instructors:
Baiocchi, M. (PI)
;
DiCiccio, C. (PI)
;
Janson, L. (PI)
;
Katsevich, G. (PI)
;
LaRocque, K. (PI)
;
Sankaran, K. (PI)
;
Xia, L. (PI)
;
Bi, N. (TA)
;
Celentano, M. (TA)
;
DiCiccio, C. (TA)
;
Janson, L. (TA)
;
Katsevich, G. (TA)
;
Khazenzon, A. (TA)
;
King, L. (TA)
;
Lampinen, A. (TA)
;
MacDonald, K. (TA)
;
Sankaran, K. (TA)
;
Shavit, Y. (TA)
;
Tong, L. (TA)
;
Wang, S. (TA)
;
Weisman, K. (TA)
STATS 50: Mathematics of Sports (MCS 100)
The use of mathematics, statistics, and probability in the analysis of sports performance, sports records, and strategy. Topics include mathematical analysis of the physics of sports and the determinations of optimal strategies. New diagnostic statistics and strategies for each sport. Corequisite:
STATS 60, 110 or 116.
Terms: not given this year

Units: 3

UG Reqs: GER:DBMath

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:
Baiocchi, M. (PI)
;
DiCiccio, C. (PI)
;
LaRocque, K. (PI)
...
more instructors for STATS 60 »
Instructors:
Baiocchi, M. (PI)
;
DiCiccio, C. (PI)
;
LaRocque, K. (PI)
;
Xia, L. (PI)
;
Bi, N. (TA)
;
Celentano, M. (TA)
;
DiCiccio, C. (TA)
;
Janson, L. (TA)
;
Katsevich, G. (TA)
;
Sankaran, K. (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)
;
Hwang, J. (PI)
;
Tay, J. (PI)
;
Wang, R. (PI)
;
Bai, Y. (TA)
;
Hwang, J. (TA)
;
Qian, J. (TA)
;
SUR, P. (TA)
;
Tsao, A. (TA)
;
Wang, X. (TA)
STATS 141: Biostatistics (BIO 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut

Units: 35

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Mukherjee, R. (PI)
;
DiCiccio, C. (TA)
;
Fukuyama, J. (TA)
...
more instructors for STATS 141 »
Instructors:
Mukherjee, R. (PI)
;
DiCiccio, C. (TA)
;
Fukuyama, J. (TA)
;
Han, X. (TA)
;
Ignatiadis, N. (TA)
;
Michael, H. (TA)
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