| STATS100 | Mathematics of Sports | This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn fr... |
| STATS101 | Data Science 101 | This course will provide a hands-on introduction to statistics and data science. Students will engage with fundamental ideas in inferential and computational thinking. Each week consists of three lectures and two labs, in which students will manipula... |
| STATS110 | 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. P... |
| STATS116 | 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.... |
| STATS141 | Biostatistics | 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... |
| STATS155 | Modern Statistics for Modern Biology | Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate me... |
| STATS160 | Introduction to Statistical Methods: Precalculus | 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... |
| STATS191 | Introduction to Applied Statistics | Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biolo... |
| STATS195 | Introduction to R | This short course runs for weeks one through four of the quarter. 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 data science with R. The goal of the sh... |
| STATS196A | Multilevel Modeling Using R | See http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, three-level analyses. |
| STATS199 | Independent Study | For undergraduates. |
| STATS200 | 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; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample t... |
| STATS202 | 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 dat... |
| STATS203 | 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. Prerequisites: A pos... |
| STATS203V | 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 offer... |
| STATS204 | Sampling | How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estim... |
| STATS205 | Introduction to Nonparametric Statistics | Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates, nearest neighbor algorithms (with non-linear features), wavelet, bootstrap. Nonparametric analogs of the one-... |
| STATS206 | Applied Multivariate Analysis | Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multi... |
| STATS207 | Introduction to Time Series Analysis | Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Pr... |
| STATS208 | Bootstrap, Cross-Validation, and Sample Re-use | By re-using the sample data, sometimes in ingenious ways, we can evaluate the accuracy of predictions, test the significance of a conclusion, place confidence bounds on an unknown parameter, select the best prediction architecture, and develop more... |
| STATS209 | Introduction to Causal Inference | This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate... |
| STATS209B | Applications of Causal Inference Methods | See http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effect... |
| STATS211 | Meta-research: Appraising Research Findings, Bias, and Meta-analysis | Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Ex... |
| STATS214 | Machine Learning Theory | How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learni... |
| STATS215 | Statistical Models in Biology | Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. T... |
| STATS216 | 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... |
| STATS216V | 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... |
| STATS217 | 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.... |
| STATS218 | Introduction to Stochastic Processes II | Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales. |
| STATS219 | Stochastic Processes | 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, sam... |
| STATS220 | Machine Learning Methods for Neural Data Analysis | With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and... |
| STATS221 | Random Processes on Graphs and Lattices | Covering modern topics in the study of random processes on graphs and lattices. Specifically, a subset of: Random walks, electrical networks and flows. Uniform spanning trees. Percolation and self-avoiding walks. Contact process, voter model and the... |
| STATS222 | Statistical Methods for Longitudinal Research | See http://rogosateaching.com/stat222/. Research designs and statistical procedures for time-ordered (repeated-measures) data. The analysis of longitudinal panel data is central to empirical research on learning, development, aging, and the effects o... |
| STATS223 | Sequential Analysis | This course will survey the history of sequential analysis from its origin in the 1940s via its continuing role in clinical trials to current activity in machine learning. Subject to the limitations of time, the following topics will be discussed: pa... |
| STATS229 | Machine Learning | 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, dens... |
| STATS237 | Investment Portfolios, Derivative Securities, and Risk Measures | Asset returns and their volatilities. Markowitz portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk and statistical models and methods for their estimation and backtesting. Financial derivatives and hedg... |
| STATS240 | 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. S... |
| STATS240P | Statistical Methods in Finance | For SCPD students; see 240. |
| STATS241 | Data-driven Financial Econometrics | (SCPD students register for 241P) Approximate dynamic programming and time series approaches in options, interest rate, and credit markets. Nonlinear least squares, nonparametric regression and model selection. Behavioral finance and efficient market... |
| STATS241P | Data-driven Financial Econometrics | For SCPD students; see STATS241. |
| STATS242 | NeuroTech Training Seminar | This is a required course for students in the NeuroTech training program, and is also open to other graduate students interested in learning the skills necessary for neurotechnology careers in academia or industry. Over the academic year, topics will... |
| STATS243 | Risk Analytics and Management in Finance and Insurance | Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard funct... |
| STATS243P | Risk Analytics and Management in Finance and Insurance | For SCPD students; see STATS243. |
| STATS244 | Quantitative Trading: Algorithms, Data, and Optimization | Statistical trading rules and performances evaluation. Active portfolio management and dynamic investment strategies. Data analytics and models of transactions data. Limit order book dynamics in electronic exchanges. Algorithmic trading, informatics,... |
| STATS244P | Quantitative Trading: Algorithms, Data and Optimization | For SCPD students; see 244. |
| STATS245 | Data, Models and Applications to Healthcare Analytics | Topics on fundamentals of data science, biological and statistical models, application to medical product safety evaluation, health risk models and their evaluation, benefit-risk assessment and multi-criteria decision analytics. Applications to envir... |
| STATS245P | Data, Models, and Applications to Healthcare Analytics | For SCPD students; see STATS245. |
| STATS248 | Causal Inference in Clinical Trials and Observational Study (II) | This course offers an overview of statistical foundations for causal inference. This course introduces new analytic methods for causal inference in observational study including propensity score, doubly robust estimation, instrumental variables, marg... |
| STATS249 | Experimental Immersion in Neuroscience | This course provides students from technical backgrounds (e.g., physics, applied physics, electrical or chemical engineering, bioengineering, computer science, statistics) the opportunity to learn how they can apply their expertise to advancing exper... |
| STATS250 | Mathematical Finance | Stochastic models of financial markets. Risk neutral pricing for derivatives, hedging strategies and management of risk. Multidimensional portfolio theory and introduction to statistical arbitrage. Prerequisite: Math 136 or equivalent. NOTE: Undergra... |
| STATS256 | Modern Statistics for Modern Biology | Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate me... |
| STATS260A | Workshop in Biostatistics | Applications of data science techniques to current problems in biology, medicine and healthcare. 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... |
| STATS260B | Workshop in Biostatistics | Applications of data science techniques to current problems in biology, medicine and healthcare. 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... |
| STATS260C | Workshop in Biostatistics | Applications of data science techniques to current problems in biology, medicine and healthcare. 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... |
| STATS261 | Intermediate Biostatistics: Analysis of Discrete Data | (Formerly HRP 261) Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, condit... |
| STATS262 | Intermediate Biostatistics: Regression, Prediction, Survival Analysis | (Formerly HRP 262) Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-mea... |
| STATS263 | Design of Experiments | Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer exp... |
| STATS264 | Foundations of Statistical and Scientific Inference | (Formerly HRP 264) The course will consist of readings and discussion of foundational papers and book sections in the domains of statistical and scientific inference. Topics to be covered include philosophy of science, interpretations of probability,... |
| STATS270 | Bayesian Statistics | 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 i... |
| STATS271 | Applied Bayesian Statistics | This course is a modern treatment of applied Bayesian statistics with a focus on high-dimensional problems. We will study a collection of canonical methods that see heavy use in applications, including high-dimensional linear and generalized linear m... |
| STATS281 | Statistical Analysis of Fine Art | This course presents the application of rigorous statistical analysis, machine learning, and data analysis to problems in the history and interpretation of fine art paintings, drawings, and other two-dimensional artworks. The course focuses on the as... |
| STATS285 | 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 environme... |
| STATS290 | Computing for Data Science | 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 co... |
| STATS298 | 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. R... |
| STATS299 | 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. |
| STATS300A | 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; Po... |
| STATS300B | Theory of Statistics II | Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: Neyman-Pearson theory; UMP tests and uniformly most accurate confidence inte... |
| STATS300C | Theory of Statistics III | Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes procedures, conjugate priors, hierarchical models. Bayesian non parametrics: diai... |
| STATS302 | Qualifying Exams Workshop | Prepares Statistics Ph.D. students for the qualifying exams by reviewing relevant course topics and problem solving strategies. |
| STATS303 | Statistics Faculty Research Presentations | For Statistics first and second year PhD students only. Discussion of statistics topics and research areas; consultation with PhD advisors. |
| STATS305A | Applied Statistics I | Statistics of real valued responses. Review of multivariate normal distribution theory. Univariate regression. Multiple regression. Constructing features from predictors. Geometry and algebra of least squares: subspaces, projections, normal equations... |
| STATS305B | Applied Statistics II | This course uses exponential family structure to motivate generalized linear models and other useful applied techniques including survival analysis methods and Bayes and empirical Bayes analyses. The lectures are based on a forthcoming book whose not... |
| STATS305C | Applied Statistics III | Methods for multivariate responses. Theory, computation, and practice for multivariate statistical tools. Topics may include multivariate Gaussian models, probabilistic graphical models, MCMC and variational Bayesian inference, dimensionality reducti... |
| STATS307 | Introduction to Time Series Analysis | Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Pr... |
| STATS310A | Theory of Probability I | Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, Borel-Cantelli lemmas, almost sure and Lp convergence, weak and strong laws of large num... |
| STATS310B | Theory of Probability II | Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 0-1 laws, Radon-Nikodym Theorem, ruin problems, etc. Other topics as time allows selected from (i) local limit theorems, (ii) renewal theory,... |
| STATS310C | Theory of Probability III | Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle, random walks, LIL and functional CLT. Markov and strong Markov property. Infini... |
| STATS311 | Information Theory and Statistics | Information theoretic techniques in probability and statistics. Fano, Assouad,and Le Cam methods for optimality guarantees in estimation. Large deviationsand concentration inequalities (Sanov's theorem, hypothesis testing, theentropy method, concent... |
| STATS314A | Advanced Statistical Theory | This course will introduce the sum-of-squares algorithmic paradigm, focusing on its applications in statistics. It will touch on a wide range of topics including clustering, robust mean estimation, robust regression, mean-field approximations of Isin... |
| STATS315A | Modern Applied Statistics: Learning | 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). B... |
| STATS315B | Modern Applied Statistics: Learning II | Two-part 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... |
| STATS316 | Stochastic Processes on Graphs | Local weak convergence, Gibbs measures on trees, cavity method, and replica symmetry breaking. Examples include random k-satisfiability, the assignment problem, spin glasses, and neural networks. Prerequisite: 310A or equivalent. https://web.stanford... |
| STATS317 | Stochastic Processes | 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 an... |
| STATS318 | Modern Markov Chains | Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, Swedson-Wong algorithms, geometric theory... |
| STATS319 | Literature of Statistics | Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit. |
| STATS32 | Introduction to R for Undergraduates | This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the shor... |
| STATS320 | Machine Learning Methods for Neural Data Analysis | With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and... |
| STATS322 | Function Estimation in White Noise | Gaussian white noise model sequence space form. Hyperrectangles, quadratic convexity, and Pinsker's theorem. Minimax estimation on Lp balls and Besov spaces. Role of wavelets and unconditional bases. Linear and threshold estimators. Oracle inequaliti... |
| STATS323 | Sequential Analysis | This course will survey the history of sequential analysis from its origin in the 1940s via its continuing role in clinical trials to current activity in machine learning. Subject to the limitations of time, the following topics will be discussed: pa... |
| STATS324 | Stein's Method | This course will teach the basics of Stein's method. The specific topics that will be covered are normal approximation, Poisson approximation, and concentration inequalities using Stein's method. If time permits, more advanced topics will be covered. |
| STATS325 | Multivariate Analysis and Random Matrices in Statistics | Topics on Multivariate Analysis and Random Matrices in Statistics. Random matrices arise frequently in modern statistical theory, and tools reflecting their properties are the basis of many statistical tests and estimation procedures. Random Matrix... |
| STATS334 | Mathematics and Statistics of Gambling | 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... |
| STATS345 | Statistical and Machine Learning Methods for Genomics | 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 mac... |
| STATS350 | Topics in Probability Theory | See http://statweb.stanford.edu/~adembo/stat-350/concentration/ Selected topics of contemporary research interest in probability theory. May be repeated once for credit. Prerequisite: 310A or equivalent. |
| STATS352 | Topics in Computing for Data Science | A seminar-style course with lectures on a range of computational topics important for modern data-intensive science, jointly supported by the Statistics department and Stanford Data Science, and suitable for advanced undergraduate/graduate students e... |
| STATS359 | Topics in Mathematical Physics | Covers a list of topics in mathematical physics. The specific topics may vary from year to year, depending on the instructor's discretion. Background in graduate level probability theory and analysis is desirable. |
| STATS361 | Causal Inference | This course covers statistical underpinnings of causal inference, with a focus on experimental design and data-driven decision making. Topics include randomization, potential outcomes, observational studies, propensity score methods, matching, double... |
| STATS362 | Topic: Monte Carlo | Random numbers and vectors: inversion, acceptance-rejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov chains, detailed balance, Metropolis-Hastings, random walk Metropolis,nnindepen... |
| STATS363 | Design of Experiments | Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer exp... |
| STATS364 | Theory and Applications of Selective Inference | This course focuses on the problem of inference under the presence of multiplicity or selection. Topics covered include classical topics multiple comparisons (FWER, FDR, FCR) as well as newer methods such as knockoffs. We will also cover inference wh... |
| STATS365 | Empirical Likelihood | Empirical likelihood (EL) allows likelihood based inferences without assuming any parametric form for the likelihood. It is based instead on reweighting the sample values. It provides data driven shapes for confidence regions and confidence bands. E... |
| STATS366 | Modern Statistics for Modern Biology | Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate me... |
| STATS367 | Statistical Models in Genetics | 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 obtaine... |
| STATS368 | Empirical Process Theory and its Applications | This course is on the theory of empirical processes. In the course we will focus on weak convergence of stochastic processes, M-estimation and empirical risk minimization. The course will cover topics like covering numbers and bracketing numbers, max... |
| STATS369 | Methods from Statistical Physics | Mathematical techniques from statistical physics have been applied with increasing success on problems form combinatorics, computer science, machine learning. These methods are non-rigorous, but in several cases they were proved to yield correct pre... |
| STATS370 | Bayesian Statistics | 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 i... |
| STATS371 | Applied Bayesian Statistics | This course is a modern treatment of applied Bayesian statistics with a focus on high-dimensional problems. We will study a collection of canonical methods that see heavy use in applications, including high-dimensional linear and generalized linear m... |
| STATS374 | Large Deviations Theory | Combinatorial estimates and the method of types. Large deviation probabilities for partial sums and for empirical distributions, Cramer's and Sanov's theorems and their Markov extensions. Applications in statistics, information theory, and statistica... |
| STATS375 | Mathematical problems in Machine Learning | Mathematical tools to understand modern machine learning systems. Generalization in machine learning, the classical view: uniform convergence, Radamacher complexity. Generalization from stability. Implicit (algorithmic) regularization. Infinite-dimen... |
| STATS376B | Topics in Information Theory and Its Applications | Information theory establishes the fundamental limits on compression and communication over networks. The tools of information theory have also found applications in many other fields, including probability and statistics, computer science and physic... |
| STATS385 | Analyses of Deep Learning | Deep learning is a transformative technology that has delivered impressive improvements in image classification and speech recognition. Many researchers are trying to better understand how to improve prediction performance and also how to improve tra... |
| STATS390 | Consulting Workshop | Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Semina... |
| STATS397 | PhD Oral Exam Workshop | For Statistics PhD students defending their dissertation. |
| STATS398 | Industrial Research for Statisticians | Doctoral research as in 399, but must be conducted for an off-campus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any follow-up projects they expect to perform is required. The re... |
| STATS399 | Research | Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit. |
| STATS48N | Riding the Data Wave | Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety... |
| STATS60 | Introduction to Statistical Methods: Precalculus | 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... |
| STATS801 | TGR Project | No Description Set |
| STATS802 | TGR Dissertation | No Description Set |