STATS 216: 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;crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered via video segments (MOOC style), and inclass problem solving sessions. 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: Win, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Mackey, L. (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; crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Google Hangout or BlueJeans. There are four homework assignments, a midterm, and final exam. 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: Sum

Units: 3

Grading: Letter or Credit/No Credit
STATS 217: Introduction to Stochastic Processes
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 instructor.
Terms: Win, Sum

Units: 23

Grading: Letter or Credit/No Credit
Instructors:
Feldheim, O. (PI)
STATS 218: Introduction to Stochastic Processes
Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Chatterjee, S. (PI)
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.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Zheng, T. (PI)
;
Jafarov, J. (TA)
STATS 221: Introduction to Mathematical Finance
Interest rate and discounted value. Financial derivatives, hedging, and risk management. Stochastic models of financial markets, introduction to Ito calculus and stochastic differential equations. BlackScholes pricing of European options. Optimal stopping and American options. Prerequisites:
MATH 53,
STATS 116, or equivalents.
Terms: not given this year

Units: 34

Grading: Letter or Credit/No Credit
STATS 222: Statistical Methods for Longitudinal Research (EDUC 351A)
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. See
http://rogosateaching.com/stat222/. Prerequisite: intermediate statistical methods
Terms: Aut

Units: 23

Grading: Letter or Credit/No Credit
Instructors:
Rogosa, D. (PI)
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, Spr

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Duchi, J. (PI)
;
Ng, A. (PI)
;
Ahluwalia, V. (TA)
;
Ahres, Y. (TA)
...
more instructors for STATS 229 »
Instructors:
Duchi, J. (PI)
;
Ng, A. (PI)
;
Ahluwalia, V. (TA)
;
Ahres, Y. (TA)
;
AlbanHidalgo, M. (TA)
;
Anenberg, B. (TA)
;
Bahtchevanov, I. (TA)
;
CHU, H. (TA)
;
CorbettDavies, S. (TA)
;
Du, Y. (TA)
;
Haque, A. (TA)
;
How, P. (TA)
;
Ishfaq, H. (TA)
;
Iyer, K. (TA)
;
Jiang, X. (TA)
;
Kaplow, I. (TA)
;
Lim, D. (TA)
;
Lin, Y. (TA)
;
Martinez, A. (TA)
;
McCann, B. (TA)
;
Parthasarathy, N. (TA)
;
Qin, J. (TA)
;
Sun, Y. (TA)
;
Vyas, S. (TA)
;
Wang, H. (TA)
;
Zhou, L. (TA)
STATS 231: Statistical Learning Theory (CS 229T)
(Same as
STATS 231) 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 online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (
STATS 315A or
CS 229). Convex optimization (
EE 364a) is helpful but not required.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Liang, P. (PI)
STATS 237: Theory of Investment Portfolios and Derivative Securities
Asset returns and their volatilities. Markowitz¿s portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk. Financial derivatives and hedging. Black¿Scholes pricing of European options. Valuation of American options. Implied volatility and the Greeks. Prerequisite:
STATS 116 or equivalent
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
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