STAT 430

Spring 2020 All Classes

All Classes

Topics in Applied Statistics

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Credit: 3 OR 4 hours.

Formulation and analysis of mathematical models for random phenomena; extensive involvement with the analysis of real data; and instruction in statistical and computing techniques as needed.

3 undergraduate hours. 4 graduate hours. May be repeated with approval. Prerequisite: STAT 410 or STAT 420; or consent of instructor.

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STAT 430 class schedule data for spring 2020
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60247	Lecture	JDG	2:00PM -2:50PM	MWF	2233 Everitt Laboratory	Douglas, J	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 4 hours Section Title: Multivariate Analysis Data Sci Section Info: TOPIC: Multivariate Analysis for Data Science Description: This is a course on applied multivariate analysis with particular attention to model-based clustering and classification methods for data science. Prerequisites: Linear Algebra, STAT 410. Credit not given for students that have already completed STAT 571. For Statistics course registration information: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
60249	Lecture	JDU	2:00PM -2:50PM	MWF	2233 Everitt Laboratory	Douglas, J	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Multivariate Analysis Data Sci Section Info: TOPIC: Multivariate Analysis for Data Science Description: This is a course on applied multivariate analysis with particular attention to model-based clustering and classification methods for data science. Prerequisites: Linear Algebra, STAT 410. Credit not given for students that have already completed STAT 571. For Statistics course registration information: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
69110	Lecture-Discussion	JJB	10:00AM -10:50AM	MWF	432 Armory	Balamuta, J	Part of Term: 1 Date Range: 01/21/20-05/06/20 Credit: 3 hours Section Title: Fundamentals of Deep Learning Section Info: Topic: Fundamentals of Deep Learning Deep Learning methods are rapidly becoming ingrained within everyday life. These methods strive to reveal patterns within the data. This course provides a foundation for developing and applying deep learning models through a study of its theory and application using a leading modeling framework. Students should be proficient in programming. Pre-Requsite: STAT 432 (possibly co-requisite) Restriction(s): Restricted to Undergrad - Urbana-Champaign.