**Instructor**

Feng Liang : liangf AT illinois DOT edu

Office: 113D Illini Hall

Phone: (217) 333-6017

**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.