## 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)
;
Erdmann-Pham, D. (PI)
;
Ghandwani, D. (PI)
...
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Instructors:
Auelua-Toomey, S. (PI)
;
Erdmann-Pham, D. (PI)
;
Ghandwani, D. (PI)
;
Jain, V. (PI)
;
Poldrack, R. (PI)
;
Auelua-Toomey, S. (TA)
;
Chuey, A. (TA)
;
Ghosh, S. (TA)
;
Howes, M. (TA)
;
Jin, Y. (TA)
;
Kanekar, R. (TA)
;
Kong, N. (TA)
;
Kubota, E. (TA)
;
Liu, S. (TA)
;
Mortazavi, L. (TA)
;
Pan, Z. (TA)
;
Paul, D. (TA)
;
Rajanala, S. (TA)
;
Rose, D. (TA)
;
Tanoh, I. (TA)
;
Wu, Y. (TA)

## STATS 101: Data Science 101

https://statweb.stanford.edu/~tibs/stat101.html This course will provide a hands-on introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Also open to graduates.

Terms: Spr, Sum
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-AQR

Instructors:
Gablenz, P. (PI)
;
Sklar, M. (PI)
;
Gablenz, P. (TA)
...
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Instructors:
Gablenz, P. (PI)
;
Sklar, M. (PI)
;
Gablenz, P. (TA)
;
Gao, S. (TA)
;
Jing, A. (TA)
;
Lu, S. (TA)
;
Paul, D. (TA)
;
Xie, R. (TA)
;
Xu, H. (TA)
;
Zhou, K. (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. Undergraduate students enroll for 5 units, graduate students enroll for 4 units. Undergraduate students must enroll in one section in addition to the main lecture. Sections are optional for graduate students.

Terms: Aut, Spr, Sum
| Units: 4-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Duchi, J. (PI)
;
Kanekar, R. (PI)
;
Palacios, J. (PI)
...
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Instructors:
Duchi, J. (PI)
;
Kanekar, R. (PI)
;
Palacios, J. (PI)
;
Wang, Y. (PI)
;
Zhang, J. (PI)
;
Dey, A. (TA)
;
Ghandwani, D. (TA)
;
Jang, J. (TA)
;
Kanekar, R. (TA)
;
Zhang, J. (TA)
;
Zhong, C. (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:
Erdmann-Pham, D. (PI)
;
Ghandwani, D. (PI)
;
Jain, V. (PI)
...
more instructors for STATS 160 »

Instructors:
Erdmann-Pham, D. (PI)
;
Ghandwani, D. (PI)
;
Jain, V. (PI)
;
Poldrack, R. (PI)
;
Auelua-Toomey, S. (TA)
;
Chuey, A. (TA)
;
Ghosh, S. (TA)
;
Howes, M. (TA)
;
Jin, Y. (TA)
;
Kanekar, R. (TA)
;
Kong, N. (TA)
;
Kubota, E. (TA)
;
Liu, S. (TA)
;
Mortazavi, L. (TA)
;
Pan, Z. (TA)
;
Paul, D. (TA)
;
Rajanala, S. (TA)
;
Rose, D. (TA)
;
Tanoh, I. (TA)
;
Wu, Y. (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)
;
Lai, T. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Schramm, T. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (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:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
...
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Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
;
GAO, Z. (TA)
;
Gablenz, P. (TA)
;
Hartog, W. (TA)
;
Hu, A. (TA)
;
Jin, Y. (TA)
;
MacKay, M. (TA)
;
Paul, D. (TA)
;
Xie, R. (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, basic computer programming knowledge, some familiarity with matrix algebra, and a pre- or co-requisite post-calculus mathematical statistics course, e.g.
STATS 200.

Terms: Sum
| Units: 3

Instructors:
Morrison, T. (PI)
;
Howes, M. (TA)

## 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

## STATS 217: Introduction to Stochastic Processes I

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking
STATS 215 instead. Prerequisite: a post-calculus introductory probability course e.g.
STATS 116

Terms: Win, Sum
| Units: 3

Instructors:
Dey, A. (PI)
;
Jain, V. (PI)
;
Jeong, Y. (TA)
;
Kanekar, R. (TA)
;
Tirlea, M. (TA)
;
Wu, Y. (TA)

## 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 to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.

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

Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Guestrin, C. (PI)
...
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Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Guestrin, C. (PI)
;
Ma, T. (PI)
;
Ng, A. (PI)
;
Re, C. (PI)
;
Ali, K. (TA)
;
Balogun, E. (TA)
;
Bhaskhar, N. (TA)
;
Chen, Y. (TA)
;
Cheong, R. (TA)
;
Chou, K. (TA)
;
Dong, K. (TA)
;
Epstein, E. (TA)
;
Hua, X. (TA)
;
Itkina, M. (TA)
;
Jain, S. (TA)
;
Jung, S. (TA)
;
Kim, K. (TA)
;
Knowles, T. (TA)
;
Kumar, A. (TA)
;
Li, K. (TA)
;
Lim, D. (TA)
;
Liu, Z. (TA)
;
Medepalli, P. (TA)
;
Nicholls, A. (TA)
;
Sachidananda, V. (TA)
;
Sridhar, A. (TA)
;
Srikanth, M. (TA)
;
Tanwar, S. (TA)
;
Thomas, G. (TA)
;
Waites, C. (TA)
;
Wolff, C. (TA)
;
Xie, M. (TA)
;
Yan, X. (TA)
;
Young, G. (TA)
;
Yuan, H. (TA)

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