STAT 525

Spring 2024 Part of Term 1

Part of Term 1
Jan 16-May 1

Topics in Computational Statistics

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Credit: 4 hours.

Various topics in computational statistics, such as optimization, Monte Carlo methods, Bayesian computation, and machine learning.

Same as CSE 525. 4 graduate hours. No professional credit. May be repeated if topics vary. Prerequisite: STAT 425, STAT 426, and STAT 510 or 511; or consent of instructor.

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STAT 525 class schedule data for spring 2024
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	48347	Lecture-Discussion	A1	9:30AM -10:50AM	TR	106B8 Engineering Hall	Wang, S	Part of Term: 1 Date Range: 01/16/24-05/01/24 Section Title: Intro to Optimization with... Section Info: Topic: Introduction to Optimization with Application to Data Analysis Description: Optimization is an essential part of modern statistics and machine learning. This course covers topics to develop scalable optimization tools for modern statistics and data analysis. It is designed to help students develop a practical understanding of how and why the existing methods work so that students can learn the core ideas of these methods and use modern statistical methods effectively. This course emphasizes theory and algorithms for smooth and non-smooth convex optimization, stochastic optimization, and their statistical applications, including Lasso, group Lasso, total variation methods, and large-scale machine learning methods. This course does not focus on programming skills training but rather on the computation complexity theories and ideas behind algorithms. This course will also provide opportunities for exploring frontier research in optimization and machine learning. A solid background in undergraduate linear algebra, analysis, and some probability are minimum prerequisites. 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.