15.077 Statistical Learning and Data Mining
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.
This class has no prerequisites.
15.077 will be offered this semester (Spring 2019). It is instructed by R. E. Welsch.
Lecture occurs 4:00 PM to 5:30 PM on Mondays and Wednesdays in E51-325.
This class counts for a total of 12 credits.
You can find more information at the MIT + 15.077 - Google Search site.
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