STAT 430

Spring 2022 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 in the same or separate terms if topics vary. Prerequisite: STAT 410; STAT 425. Some topics may require additional prerequisites. Read the section text for each topic.

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STAT 430 class schedule data for spring 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
63950	Lecture-Discussion	JBG	11:00AM -12:20PM	TR	106B8 Engineering Hall	Balamuta, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Fundamentals of Deep Learning Section Info: 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. Prerequsite: STAT 432 (possibly co-requisite). For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
69110	Lecture-Discussion	JBU	11:00AM -12:20PM	TR	106B8 Engineering Hall	Balamuta, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 3 hours Section Title: Fundamentals of Deep Learning Section Info: 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. Prerequsite: STAT 432 (possibly co-requisite). For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Undergrad - Urbana-Champaign.
69375	Lecture-Discussion	PMG	11:00AM -12:20PM	TR	2101 Everitt Laboratory	Martinez Vargas, P	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Infectious Diseases Modeling Section Info: This course will introduce basic concepts of infectious disease modeling and epidemiology, with an emphasis on the use of mathematical models. The topics that are going to be covered include infectious disease studies, the transmission dynamics of human pathogens with different transmission routes, modeling approaches to understand disease outbreaks, ways in which mathematical models can be used to understand the mechanisms driving infectious disease dynamics, and the impacts of public health interventions. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69376	Lecture-Discussion	PMU	11:00AM -12:20PM	TR	2101 Everitt Laboratory	Martinez Vargas, P	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 3 hours Section Title: Infectious Diseases Modeling Section Info: This course will introduce basic concepts of infectious disease modeling and epidemiology, with an emphasis on the use of mathematical models. The topics that are going to be covered include infectious disease studies, the transmission dynamics of human pathogens with different transmission routes, modeling approaches to understand disease outbreaks, ways in which mathematical models can be used to understand the mechanisms driving infectious disease dynamics, and the impacts of public health interventions. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
72306	Lecture-Discussion	VEG	12:00PM -12:50PM	MWF	106B8 Engineering Hall	Ellison, T	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Unsupervised Learning Section Info: This will be an applied course in unsupervised learning. This course will survey some of the most commonly used clustering algorithms and dimensionality reduction algorithms currently used by data scientists. Students will apply these algorithms to datasets in Python. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Graduate - Urbana-Champaign.
72307	Lecture-Discussion	VEU	12:00PM -12:50PM	MWF	106B8 Engineering Hall	Ellison, T	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 3 hours Section Title: Unsupervised Learning Section Info: This will be an applied course in unsupervised learning. This course will survey some of the most commonly used clustering algorithms and dimensionality reduction algorithms currently used by data scientists. Students will apply these algorithms to datasets in Python. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration Restriction(s): Restricted to Undergrad - Urbana-Champaign.