## 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:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Kong, N. (PI)
...
more instructors for STATS 60 »

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Kong, N. (PI)
;
Lemhadri, I. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Zhu, X. (PI)
;
Azadkia, M. (TA)
;
Bhattacharya, S. (TA)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Friedberg, R. (TA)
;
Gupta, S. (TA)
;
Han, K. (TA)
;
Harrison, M. (TA)
;
Khan, S. (TA)
;
Lemhadri, I. (TA)
;
Rajanala, S. (TA)
;
Ren, Z. (TA)
;
Schwartz, J. (TA)

## 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: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Donoho, D. (PI)
;
Tirlea, M. (PI)
;
Wong, W. (PI)
;
GAO, Z. (TA)
;
Jang, J. (TA)
;
Kunnasagaran, A. (TA)
;
Misiakiewicz, T. (TA)
;
Tirlea, M. (TA)
;
Wu, H. (TA)

## STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

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

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Harrison, M. (PI)
...
more instructors for STATS 160 »

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Harrison, M. (PI)
;
Kong, N. (PI)
;
Lemhadri, I. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Walters, J. (PI)
;
Zhu, X. (PI)
;
Azadkia, M. (TA)
;
Bhattacharya, S. (TA)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Friedberg, R. (TA)
;
Gupta, S. (TA)
;
Han, K. (TA)
;
Khan, S. (TA)
;
Lemhadri, I. (TA)
;
Rajanala, S. (TA)
;
Ren, Z. (TA)

## STATS 199: Independent Study

For undergraduates.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable for credit

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
...
more instructors for STATS 199 »

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Lai, T. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)

## STATS 202: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).

Terms: Aut, Sum
| Units: 3

Instructors:
Tran, L. (PI)
;
Walther, G. (PI)
;
Ghosh, S. (TA)
;
Gibbs, I. (TA)
;
Liu, S. (TA)
;
Rajanala, S. (TA)
;
Wang, X. (TA)
;
Wang, Y. (TA)
;
Wu, H. (TA)
;
Zhong, C. (TA)

## STATS 203V: Introduction to Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. This course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. Prerequisites: a post-calculus introductory probability course, e.g.
STATS 116. In addition, a co-requisite post-calculus mathematical statistics course, e.g.
STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.

Terms: Sum
| Units: 3

Instructors:
Ray, S. (PI)

## STATS 216V: Introduction to Statistical Learning

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. There are four homework assignments, a midterm, and a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).

Terms: Sum
| Units: 3

Instructors:
Gupta, S. (PI)

## STATS 229: Machine Learning (CS 229)

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/numpy, familiarity with probability theory to the equivalency of CS109 or
STATS116, and familiarity with multivariable calculus and linear algebra to the equivalency of
MATH51.

Terms: Aut, Spr, Sum
| Units: 3-4

Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Ma, T. (PI)
;
Ng, A. (PI)
;
Re, C. (PI)
;
Arad Hudson, D. (TA)
;
Caron, P. (TA)
;
Chen, E. (TA)
;
Chen, Y. (TA)
;
Ding, T. (TA)
;
Jia, Z. (TA)
;
Jiang, Q. (TA)
;
Jin, Y. (TA)
;
Khani, F. (TA)
;
Khosla, K. (TA)
;
Ko, M. (TA)
;
Kurenkov, A. (TA)
;
Li, J. (TA)
;
She, J. (TA)
;
Smit, A. (TA)
;
Steinberg, E. (TA)
;
Tlili, F. (TA)
;
Wang, G. (TA)
;
Xiong, Z. (TA)
;
Yang, J. (TA)
;
Zhang, K. (TA)
;
Zhang, V. (TA)
;
Zhu, M. (TA)

## STATS 298: Industrial Research for Statisticians

Masters-level research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an off-campus employer. Students must submit a written final report upon completion of the internship in order to receive credit. Repeatable for credit. Prerequisite: enrollment in Statistics M.S. program.

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable for credit

Instructors:
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Rothenhaeusler, D. (PI)
...
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Instructors:
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Rothenhaeusler, D. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)

## STATS 299: Independent Study

For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.

Terms: Aut, Win, Spr, Sum
| Units: 1-5
| Repeatable for credit

Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Duchi, J. (PI)
...
more instructors for STATS 299 »

Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Linderman, S. (PI)
;
Rogosa, D. (PI)
;
Rothenhaeusler, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)