STAT 432

Fall 2018 All Classes

All Classes

Basics of Statistical Learning

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Credit: 3 OR 4 hours.

Topics in supervised and unsupervised learning are covered, including logistic regression, support vector machines, classification trees and nonparametric regression. Model building and feature selection are discussed for these techniques, with a focus on regularization methods, such as lasso and ridge regression, as well as methods for model selection and assessment using cross validation. Cluster analysis and principal components analysis are introduced as examples of unsupervised learning.

Same as ASRM 451. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400, and either STAT 420 or STAT 425.

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STAT 432 class schedule data for fall 2018
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
70220	Lecture-Discussion	1GR	8:00AM -8:50AM	MWF	103 Transportation Building	Zhu, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Restriction(s): Restricted to Graduate - Urbana-Champaign.
70221	Lecture-Discussion	1UG	8:00AM -8:50AM	MWF	103 Transportation Building	Zhu, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Restriction(s): Restricted to Undergrad - Urbana-Champaign.
70222	Lecture-Discussion	2GR	9:00AM -9:50AM	MWF	112 Transportation Building	Zhu, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 4 hours Restriction(s): Restricted to Graduate - Urbana-Champaign.
70223	Lecture-Discussion	2UG	9:00AM -9:50AM	MWF	112 Transportation Building	Zhu, R	Part of Term: 1 Date Range: 08/27/18-12/12/18 Credit: 3 hours Restriction(s): Restricted to Undergrad - Urbana-Champaign.