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1 - 10 of 17 results for: STATS

STATS 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Satisfactory/No Credit

STATS 202: Data Mining and Analysis

Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit

STATS 203: 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. Pre- or corequisite: 200.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit

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 Google Hangout or BlueJeans. 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 | Grading: Letter or Credit/No Credit
Instructors: Hastie, T. (PI)

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: STATS 116 or consent of instructor.
Terms: Win, Sum | Units: 2-3 | Grading: Letter or Credit/No Credit

STATS 237: Theory of Investment Portfolios and Derivative Securities

Asset returns and their volatilities. Markowitz 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
Instructors: Tsang, K. (PI)

STATS 237P: Theory of Investment Portfolios and Derivative Securities

For SCPD students; see STATS237
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tsang, K. (PI)

STATS 245: Data, Models, and Decision Analytics

Statistical models and decision theory. Online A/B testing, comparative effective studies of medical treatments. Introduction to recommender systems in online services, personalized medicine and marketing. Prerequisite or corequisite: STATS 202, or CS 229, or CME 250, or equivalent.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tsang, K. (PI)

STATS 245P: Data, Models, and Decision Analytics

For SCPD students; see STATS245.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Tsang, K. (PI)

STATS 298: 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. Repeatable for credit. Prerequisite: enrollment in Statistics M.S. program.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
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