Instructor
Feng Liang : liangf AT illinois DOT edu
Office: 113D Illini Hall
Phone: (217) 333-6017

Registration (F23)
The instructor cannot manually enroll any students. If you have any questions related to registration and enrollment of STAT 542, please contact the registration office.

• For non-STAT students, registration is open during the first week of Fall 2023 instruction.
• An online version of STAT 542, usually offered in the Fall, is designed for the Online Master of Computer Science in Data Science (MCS-DS) and is NOT open to UIUC students outside that program.

Text

• ” The Elements of Statistical Learning: Data Mining, Inference and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
• “An Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Slides, videos and solutions can be found here.

Lecture [S19]

Prerequisites
Knowledge of basic multivariate calculus, statistical inference, and linear algebra. You should be comfortable with the following concepts: probability distribution functions, expectations, conditional distributions, likelihood functions, random samples, estimators and linear regression models.

I would suggest non-stat students to pick up some basic knowledge of statistical inference and data analysis, from Wiki pages, online lecture notes, and textbooks for courses at the level of STAT 410 / 425 and STAT 432.

Course Description
This course provides an introduction to modern techniques for statistical analysis of complex and massive data. Examples of these are model selection for regression/classification, nonparametric models including splines and kernel models, regularization, model ensemble, recommender system, and clustering analysis. Applications are discussed as well as computation and theoretical foundations.

Computing
The homework assignments and the projects will involve some computing. You are expected to have some prior programming experience with either R or Python.