STATS 32:
Introduction to R for Undergraduates
This short course runs for weeks two through five of the quarter. It is recommended for undergraduate students who want to use R in the linguistics, humanities, social sciences or biological sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to nonengineering students. Laptops necessary for use in class.
Terms: Aut

Units: 1

Grading: Satisfactory/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: ;
Choi, A. (PI);
DiCiccio, C. (PI);
Poldrack, R. (PI);
Sklar, M. (PI);
Xia, L. (PI);
Bhattacharya, S. (TA);
Cao, S. (TA);
Greaves, D. (TA);
Guan, L. (TA);
Lemhadri, I. (TA);
Panigrahi, S. (TA);
Roquero Gimenez, J. (TA);
SUR, P. (TA);
Sesia, M. (TA)
STATS 101:
Data Science 101
http://web.stanford.edu/class/stats101/ . This course will provide a handson 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 realworld 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. Open to graduates as well.
Terms: Aut, Spr, Sum

Units: 5

UG Reqs: GER: DBNatSci, WAYAQR

Grading: Letter or Credit/No Credit
Instructors: ;
DiCiccio, C. (PI);
Mohanty, P. (PI);
Sabatti, C. (PI);
Taylor, J. (PI);
Walther, G. (PI);
Xia, L. (PI);
Bhattacharya, S. (TA);
Cauchois, M. (TA);
Du, W. (TA);
GAO, Z. (TA);
Misiakiewicz, T. (TA);
Qian, J. (TA);
Tuzhilina, E. (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);
Kaluwa Devage, P. (PI);
Zhang, Y. (PI);
Bi, N. (TA);
Cao, S. (TA);
Cauchois, M. (TA);
SUR, P. (TA);
YAN, J. (TA);
YANG, J. (TA);
Zhang, A. (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, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

Grading: Letter or Credit/No Credit
Instructors: ;
Choi, A. (PI);
DiCiccio, C. (PI);
Poldrack, R. (PI);
Sklar, M. (PI);
Xia, L. (PI);
Bhattacharya, S. (TA);
Cao, S. (TA);
Greaves, D. (TA);
Guan, L. (TA);
Lemhadri, I. (TA);
Panigrahi, S. (TA);
Roquero Gimenez, J. (TA);
SUR, P. (TA);
Sesia, M. (TA)
STATS 195:
Introduction to R (CME 195)
This short course runs for four weeks beginning in the second week of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
STATS 199:
Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors: ;
Baiocchi, M. (PI);
Candes, E. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Jackman, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Mackey, L. (PI);
Montanari, A. (PI);
Mukherjee, R. (PI);
Owen, A. (PI);
Palacios, J. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 200:
Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116. http://statweb.stanford.edu/~sabatti/Stat200/index.html
Terms: Aut, Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors: ;
Mohanty, P. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Bhattacharya, S. (TA);
Bi, N. (TA);
Ghosh, S. (TA);
Gupta, S. (TA);
Hamidi, N. (TA);
Hwang, J. (TA);
Li, S. (TA);
Misiakiewicz, T. (TA);
Ren, Z. (TA);
Roquero Gimenez, J. (TA)
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

Grading: Letter or Credit/No Credit
Instructors: ;
Patel, R. (PI);
Walther, G. (PI);
Achanta, R. (TA);
Feldman, M. (TA);
Ghosh, S. (TA);
Gupta, S. (TA);
Markovic, J. (TA);
Misiakiewicz, T. (TA);
Orenstein, P. (TA);
Patterson, E. (TA);
Qian, J. (TA);
Ruan, F. (TA);
Tsao, A. (TA);
Tuzhilina, E. (TA);
YAN, J. (TA);
Zhang, A. (TA);
Zhong, C. (TA)
STATS 206:
Applied Multivariate Analysis
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computerintensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre or corequisite: 200.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
STATS 219:
Stochastic Processes (MATH 136)
Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite: STATS 116 or MATH 151 or equivalent. Recommended: MATH 115 or equivalent. http://statweb.stanford.edu/~adembo/math136/
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
STATS 222:
Statistical Methods for Longitudinal Research (EDUC 351A)
See http://rogosateaching.com/stat222/. Research designs and statistical procedures for timeordered (repeatedmeasures) data. The analysis of longitudinal panel data is central to empirical research on learning, development, aging, and the effects of interventions. Topics include: measurement of change, growth curve models, analysis of durations including survival analysis, experimental and nonexperimental group comparisons, reciprocal effects, stability. Prerequisite: intermediate statistical methods
Terms: Aut

Units: 23

Grading: Letter or Credit/No Credit
STATS 229:
Machine Learning (CS 229)
Topics: statistical pattern recognition, linear and nonlinear regression, nonparametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Terms: Aut

Units: 34

Grading: Letter or Credit/No Credit
Instructors: ;
Boneh, D. (PI);
Ng, A. (PI);
Qu, S. (PI);
Avati, A. (TA);
Cho, P. (TA);
Dery, L. (TA);
Dwaracherla, V. (TA);
Genthial, G. (TA);
Haque, A. (TA);
Heereguppe Radhakrishna, S. (TA);
Huang, J. (TA);
Irvin, J. (TA);
Jiang, Q. (TA);
Koochak, Z. (TA);
Le Calonnec, Y. (TA);
Legros, F. (TA);
Li, H. (TA);
Liu, V. (TA);
Liu, X. (TA);
Mahajan, A. (TA);
Mehra, S. (TA);
Meng, C. (TA);
Oshri, B. (TA);
Patil, I. (TA);
Sankar, V. (TA);
Townshend, R. (GP);
Voisin, M. (TA);
Wu, Y. (TA);
Xie, Z. (TA);
Yue, C. (TA);
Zhang, B. (TA)
STATS 240:
Statistical Methods in Finance
(SCPD students register for 240P.) Regression analysis and applications to investment models. Principal components and multivariate analysis. Likelihood inference and Bayesian methods. Financial time series. Estimation and modeling of volatilities. Statistical methods for portfolio management. Prerequisite: STATS 200 or equivalent.
Terms: Aut

Units: 34

Grading: Letter or Credit/No Credit
STATS 240P:
Statistical Methods in Finance
For SCPD students; see 240.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
STATS 260A:
Workshop in Biostatistics (BIODS 260A)
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Aut

Units: 12

Repeatable for credit

Grading: Medical Satisfactory/No Credit
STATS 285:
Massive Computational Experiments, Painlessly
Ambitious Data Science requires massive computational experimentation; the entry ticket for a solid PhD in some fields is now to conduct experiments involving 1 Million CPU hours. Recently several groups have created efficient computational environments that make it painless to run such massive experiments. This course reviews stateoftheart practices for doing massive computational experiments on compute clusters in a painless and reproducible manner. Students will learn how to automate their computing experiments first of all using nutsandbolts tools such as Perl and Bash, and later using available comprehensive frameworks such as ClusterJob and CodaLab, which enables them to take on ambitious Data Science projects. The course also features few guest lectures by renowned scientists in the field of Data Science. Students should have a familiarity with computational experiments and be facile in some highlevel computer language such as R, Matlab, or Python.
Terms: Aut

Units: 12

Grading: Satisfactory/No Credit
STATS 298:
Industrial Research for Statisticians
Masterslevel research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an offcampus 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

Grading: Satisfactory/No Credit
Instructors: ;
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Ioannidis, J. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Liang, P. (PI);
Montanari, A. (PI);
Olshen, R. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI);
Walther, G. (PI);
Wong, W. (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: 110

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors: ;
Baiocchi, M. (PI);
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Montanari, A. (PI);
Narasimhan, B. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 300A:
Theory of Statistics I
Finite sample optimality of statistical procedures; Decision theory: loss, risk, admissibility; Principles of data reduction: sufficiency, ancillarity, completeness; Statistical models: exponential families, group families, nonparametric families; Point estimation: optimal unbiased and equivariant estimation, Bayes estimation, minimax estimation; Hypothesis testing and confidence intervals: uniformly most powerful tests, uniformly most accurate confidence intervals, optimal unbiased and invariant tests. Prerequisites: Real analysis, introductory probability (at the level of STATS 116), and introductory statistics.
Terms: Aut

Units: 23

Grading: Letter or Credit/No Credit
STATS 303:
PhD First Year Student Workshop
For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
STATS 305A:
Introduction to Statistical Modeling
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage & influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
STATS 310A:
Theory of Probability I (MATH 230A)
Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, BorelCantelli lemmas, almost sure and Lp convergence, weak and strong laws of large numbers. Large deviations. Weak convergence; central limit theorems; Poisson convergence; Stein's method. Prerequisites: 116, MATH 171.
Terms: Aut

Units: 24

Grading: Letter or Credit/No Credit
STATS 314A:
Advanced Statistical Theory
Covers a range of topics, including: empirical processes, asymptotic efficiency, uniform convergence of measures, contiguity, resampling methods, Edgeworth expansions.
Terms: not given this year

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
STATS 316:
Stochastic Processes on Graphs
Local weak convergence, Gibbs measures on trees, cavity method, and replica symmetry breaking. Examples include random ksatisfiability, the assignment problem, spin glasses, and neural networks. Prerequisite: 310A or equivalent. https://web.stanford.edu/~montanar/TEACHING/Stat316/stat316.html
Terms: Aut

Units: 13

Grading: Letter or Credit/No Credit
STATS 319:
Literature of Statistics
Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
Terms: Aut, Win, Spr

Units: 13

Repeatable for credit

Grading: Satisfactory/No Credit
STATS 350:
Topics in Probability Theory
See http://statweb.stanford.edu/~adembo/stat350/concentration/ Selected topics of contemporary research interest in probability theory. May be repeated once for credit. Prerequisite: 310A or equivalent.
Terms: Aut

Units: 13

Repeatable for credit

Grading: Letter or Credit/No Credit
STATS 385:
Theories of Deep Learning
The spectacular recent successes of deep learning are purely empirical. Nevertheless intellectuals always try to explain important developments theoretically. In this literature course we will review recent work of Burna and Mallat, Mhaskar and Poggio, Papyan and Elad, Bolsckei and coauthors, Baraniuk and coauthors, and others, seeking to build theoretical frameworks deriving deep networks as consequences. After initial background lectures, we will have some of the authors presenting lectures on specific papers.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
STATS 390:
Consulting Workshop
Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's dropin consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.
Terms: Aut, Win, Spr, Sum

Units: 13

Repeatable for credit

Grading: Satisfactory/No Credit
STATS 398:
Industrial Research for Statisticians
Doctoral research as in 399, but must be conducted for an offcampus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any followup projects they expect to perform is required. The report is due at the end of the quarter in which the course is taken. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors: ;
Candes, E. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Romano, J. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 399:
Research
Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 110

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors: ;
Baiocchi, M. (PI);
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Liang, P. (PI);
Mackey, L. (PI);
Montanari, A. (PI);
Mukherjee, R. (PI);
Narasimhan, B. (PI);
Owen, A. (PI);
Palacios, J. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI);
Walther, G. (PI);
Wickham, H. (PI);
Wong, W. (PI)
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Candes, E. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 802:
TGR Dissertation
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Candes, E. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Duffie, D. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Liang, P. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tian, L. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)