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Title  Instructor(s)  Quarter  Day, Time, Location 

A Course in Bayesian Statistics (STATS 270) STATS 370 (section 1) This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will... 
Wong, W., Orenstein, P.  20172018 Winter 
Tuesday Thursday 3:00pm  4:20pm Green Earth Sciences150 
A Course in Bayesian Statistics (STATS 370) STATS 270 (section 1) This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will... 
Wong, W., Orenstein, P.  20172018 Winter 
Tuesday Thursday 3:00pm  4:20pm Green Earth Sciences150 
Advanced Statistical Methods for Observational Studies (CHPR 290, EDUC 260B, HRP 292) STATS 266 (section 1) Design principles and statistical methods for observational studies. Topics include: matching methods, sensitivity analysis, and instrumental variables. 3 unit registration requires a small project and presentation. Computing is in R. Prerequisites: HRP 261 and 262 or STAT 209 ( HRP 239), or... 
Rogosa, D., Baiocchi, M.  20172018 Spring 
Monday 2:30pm  4:20pm MSOBX303 
Biostatistics (BIO 141) STATS 141 (section 1) 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... 
Du, W., Zhu, X., Deng, B., Han, X., Tuzhilina, E.  20172018 Winter 
Tuesday Thursday 9:00am  10:20am 200002 
Computing for Data Science STATS 290 (section 1) Programming and computing techniques for the requirements of data science: acquisition and organization of data; visualization, modelling and inference for scientific applications; presentation and interactive communication of results. Emphasis on computing for substantial projects. Software... 
Qian, J., Liu, K., Ignatiadis, N., Chambers, J., Narasimhan, B.  20172018 Winter 
Monday Wednesday Friday 10:30am  11:20am NVIDIA Auditorium 
Consulting Workshop STATS 390 (section 1) 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... 
Siegmund, D.  20172018 Spring 
Friday 12:30pm  1:20pm Sequoia Hall 200 
Consulting Workshop STATS 390 (section 1) 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... 
Johndrow, J.  20172018 Winter 
Friday 12:30pm  1:20pm Sequoia Hall 200 
Data Science 101 STATS 101 (section 1) 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... 
Sabatti, C., Walther, G.  20172018 Spring 
Monday Tuesday Wednesday Thursday Friday 9:30am  10:20am 300300 
Information Theory (EE 376A) STATS 376A (section 1) The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and... 
Weissman, T., Han, Y., Tatwawadi, K.  20172018 Winter 
Tuesday Thursday 12:00pm  1:20pm Gates B1 
Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261) STATS 261 (section 1) Methods for analyzing data from casecontrol and crosssectional studies: the 2x2 table, chisquare test, Fisher's exact test, odds ratios, MantelHaenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS.... 
Sainani, K.  20172018 Winter 
Monday Wednesday 11:30am  12:50pm Alway Building, Room M112 
Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262) STATS 262 (section 1) Methods for analyzing longitudinal data. Topics include KaplanMeier methods, Cox regression, hazard ratios, timedependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeatedmeasures ANOVA, GEE, and mixed models. Emphasis is on practical... 
Sainani, K.  20172018 Spring 
Monday Wednesday 1:30pm  2:50pm Li Ka Shing Center, room 120 
Introduction to Applied Statistics STATS 191 (section 1) Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and crossvalidation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects... 
Hwang, J., Zhang, Y., Ghosh, S., GAO, Z., Walther, G.  20172018 Winter 
Monday Wednesday Friday 9:30am  10:20am Gates B1 
Introduction to R (CME 195) STATS 195 (section 1) 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... 
Sesia, M.  20172018 Spring 
Tuesday Thursday 12:00pm  1:20pm Hewlett Teaching Center Rm 101 
Introduction to Regression Models and Analysis of Variance STATS 203 (section 1) 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. Pre or corequisite: 200. 
Johndrow, J., Greaves, D., Gupta, S.  20172018 Winter 
Tuesday Thursday 1:30pm  2:50pm 200303 
Introduction to Regression Models and Analysis of Variance STATS 203 (section 1) 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. Pre or corequisite: 200. 
Johndrow, J., Greaves, D., Gupta, S.  20172018 Winter 
Tuesday Thursday 1:30pm  2:50pm 200303 
Introduction to Regression Models and Analysis of Variance STATS 203 (section 1) 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. Pre or corequisite: 200. 
Johndrow, J.  20172018 Winter 
Tuesday Thursday 1:30pm  2:50pm 200303 
Introduction to Statistical Inference STATS 200 (section 1) 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.... 
Sabatti, C., Bi, N., Mohanty, P., Ren, Z., Misiakiewicz, T., Li, S.  20172018 Winter 
Monday Wednesday Friday 11:30am  12:20pm 370370 
Introduction to Statistical Learning STATS 216 (section 1) Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso);... 
Tibshirani, R., Tay, J., Feldman, M., Walsh, D., Donnat, C., Zhao, Q.  20172018 Winter 
Monday Wednesday 3:00pm  4:20pm Gates B1 
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160) STATS 60 (section 1) 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... 
Poldrack, R.  20172018 Winter 
Monday Wednesday Friday 10:30am  11:50am 420040 
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160) STATS 60 (section 1) 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... 
Xia, L.  20172018 Spring 
Monday Tuesday Wednesday Thursday Friday 9:30am  10:20am 420040 
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60) STATS 160 (section 1) 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... 
Xia, L.  20172018 Spring 
Monday Tuesday Wednesday Thursday Friday 9:30am  10:20am 420040 
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60) STATS 160 (section 1) 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... 
Poldrack, R.  20172018 Winter 
Monday Wednesday Friday 10:30am  11:50am 420040 
Introduction to Stochastic Processes I STATS 217 (section 1) Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. NonStatistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of... 
Tsao, A., Zhang, Y., Cao, S.  20172018 Winter 
Monday Wednesday Friday 9:30am  10:20am 200205 
Introduction to Stochastic Processes I STATS 217 (section 1) Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. NonStatistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of... 
20172018 Winter 
Monday Wednesday Friday 9:30am  10:20am 200205 

Introduction to Stochastic Processes II STATS 218 (section 1) Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales. 
Chatterjee, S.  20172018 Spring 
Tuesday Thursday 10:30am  11:50am Sequoia Hall 200 
Introduction to the Bootstrap STATS 208 (section 1) The bootstrap is a computerbased method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and... 
Donoho, D.  20172018 Spring 
Tuesday Thursday 9:00am  10:20am 5052H 
Introduction to Time Series Analysis STATS 207 (section 1) Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and statespace models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the... 
Donoho, D.  20172018 Spring 
Tuesday Thursday 1:30pm  2:50pm 380380C 
Literature of Statistics STATS 319 (section 1) Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit. 
Romano, J.  20172018 Spring 
Wednesday 11:30am  12:20pm Gates B12 
Literature of Statistics STATS 319 (section 1) Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit. 
Siegmund, D.  20172018 Winter 
Monday 1:30pm  2:50pm 380381T 
Mathematical Finance (MATH 238) STATS 250 (section 1) Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. Hedging strategies and management of risk. Term structure models and interest rate derivatives. Optimal stopping and American options. Corequisites: MATH 236 and 227 or... 
Papanicolaou, G.  20172018 Winter 
Tuesday Thursday 1:30pm  2:50pm 380380W 
Mathematics and Statistics of Gambling (MATH 231) STATS 334 (section 1) Probability and statistics are founded on the study of games of chance. Nowadays, gambling (in casinos, sports and the Internet) is a huge business. This course addresses practical and theoretical aspects. Topics covered: mathematics of basic random phenomena (physics of coin tossing and... 
Diaconis, P.  20172018 Spring 
Tuesday Thursday 3:00pm  4:20pm 200030 
Mathematics of Sports (MCS 100) STATS 50 (section 1) 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.... 
DiCiccio, C.  20172018 Spring 
Monday Wednesday Friday 2:30pm  3:20pm 380380F 
Metaresearch: Appraising Research Findings, Bias, and Metaanalysis (CHPR 206, HRP 206, MED 206) STATS 211 (section 1) Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Metaanalysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics,... 
Serghiou, S., Ioannidis, J.  20172018 Winter 
Friday 9:30am  12:20pm 60109 
Methods for Applied Statistics I: Exponential Families in Theory and Practice STATS 305B (section 1) Exponential families are central to parametric statistical inference. This course emphasizes the applied aspects of exponential family theory, with special emphasis on Generalized Linear Models. Prerequisite: 305A or equivalent. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A... 
Efron, B., Cai, F., Roquero Gimenez, J.  20172018 Winter 
Monday Wednesday Friday 1:30pm  2:20pm Sequoia Hall 200 
Methods for Applied Statistics II: Applied Multivariate Statistics STATS 305C (section 1) Theory, computational aspects, and practice of a variety of important multivariate statistical tools for data analysis. Topics include classical multivariate Gaussian and undirected graphical models, graphical displays. PCA, SVD and generalizations including canonical correlation analysis,... 
Hastie, T.  20172018 Spring 
Monday Wednesday 11:30am  1:20pm Sequoia Hall 200 
Modern Applied Statistics: Data Mining STATS 315B (section 1) Twopart sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive... 
Friedman, J.  20172018 Spring 
Tuesday Thursday 1:30pm  2:50pm Gates B1 
Modern Applied Statistics: Learning STATS 315A (section 1) Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization.... 
Tibshirani, R., Friedberg, R., Arthur, J., Markovic, J., Katsevich, G., Sesia, M.  20172018 Winter 
Monday Wednesday 1:30pm  2:50pm 260113 
Multilevel Modeling Using R (EDUC 401D) STATS 196A (section 1) See http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: twolevel nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, threelevel analyses.... 
Rogosa, D.  20172018 Spring 
Wednesday 3:30pm  5:20pm Littlefield 104 
Network Information Theory (EE 376B) STATS 376B (section 1) Network information theory deals with the fundamental limits on information flow in networks and the optimal coding schemes that achieve these limits. It aims to extend Shannon's pointtopoint information theory and the FordFulkerson maxflow mincut theorem to networks with multiple sources... 
El Gamal, A.  20172018 Spring 
Tuesday Thursday 9:00am  10:20am Herrin T185 
PhD First Year Student Workshop STATS 303 (section 1) For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor. 
Candes, E.  20172018 Spring 
Monday Wednesday 3:00pm  4:20pm 380380D 
PhD First Year Student Workshop STATS 303 (section 1) For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor. 
Candes, E.  20172018 Winter 
Monday Wednesday 11:30am  1:20pm Sequoia Hall 200 
PhD First Year Student Workshop STATS 303 (section 1) For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor. 
Candes, E.  20172018 Spring 
Monday Wednesday Friday 11:30am  1:20pm Sequoia Hall 200 
PhD First Year Student Workshop STATS 303 (section 1) For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor. 
Candes, E.  20172018 Winter 
Monday Wednesday 11:30am  1:20pm Sequoia Hall 200 
Readings in Applied Data Science STATS 337 (section 1) Weekly readings and discussion of applied data science topics. Data wrangling, tidy data, and database basics. Visualization for exploration and explanation. The intersection of software engineering and data science: continuous integration, unit testing, and documentation. Reproducible research... 
Wickham, H.  20172018 Spring 
Monday 1:30pm  2:50pm 160314 
Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, GENE 245) STATS 345 (section 1) Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and... 
20172018 Spring 
Monday 10:30am  11:50am 

Statistical Learning Theory (CS 229T) STATS 231 (section 1) How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include classical asymptotics, method of... 
Duchi, J.  20172018 Spring 
Monday Wednesday 3:00pm  4:20pm 200034 
Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239) STATS 209 (section 1) See http://rogosateaching.com/stat209/. Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes... 
Rogosa, D., Sklar, M.  20172018 Winter 
Wednesday Friday 2:30pm  4:20pm Sequoia Hall 200 
Statistical Models in Biology STATS 215 (section 1) Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the WrightFisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as... 
Siegmund, D.  20172018 Spring 
Tuesday Thursday 1:30pm  2:50pm Hewlett Teaching Center 103 
Statistical Models in Genetics STATS 367 (section 1) This course will cover statistical problems in population genetics and molecular evolution with an emphasis on coalescent theory. Special attention will be paid to current research topics, illustrating the challenges presented by genomic data obtained via highthroughput technologies. No prior... 
Palacios, J.  20172018 Winter 
Tuesday Thursday 10:30am  11:50am STLC118 
Stochastic Processes STATS 317 (section 1) Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging. 
Taylor, J.  20172018 Winter 
Tuesday Thursday 1:30pm  2:50pm 420050 