MIT Subject Listing & Schedule
IAP / Spring 2019 Search Results

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1 subject found.

IDS.147[J] Statistical Learning and Data Mining

()
(Same subject as 15.077[J])
Prereq: Permission of instructor
Units: 4-0-8
Lecture: MW4-5.30 (E51-325) Recitation: T4 (E51-145)

Advanced introduction to theory and application of statistics, data-mining, and machine learning, concentrating on techniques used in management science, marketing, finance, consulting, engineering systems, and bioinformatics. Topics include the bootstrap theory of estimation, testing, nonparametric statistics, analysis of variance, categorical data analysis, regression analysis, MCMC, EM, Gibbs sampling, and Bayesian methods. Focuses on data mining, supervised learning, and multivariate analysis. Topics selected from logistic regression; principal components and dimension reduction; discrimination and classification analysis, trees (CART), partial least squares, nearest neighbors, regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software packages, e.g., R and MATLAB. Some background in statistics required. Includes term project.
R. Welsch
Textbooks (Spring 2019)