Feng Liang : liangf AT illinois DOT edu
Office: 113D Illini Hall
Phone: (217) 333-6017
- “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.
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.
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.
The homework assignments and the projects will involve some computing. You are expected to have some prior programming experience with either R or Python.