Stat 542: Statistical Learning
-
Instructor:
- Feng Liang : liangf AT uiuc DOT edu
Office: 116A Illini Hall
Office hours: Tuesday, 3-4pm or by appointment
- TuTh, 10:30-11:50am, Room 2 Illini Hall
First lecture: Tuesday, August 26, 2008
Teaching Assistant:
- Jianfeng Xu: jxu9 AT uiuc DOT edu
Office hours: Monday, 1-2pm, Room 104 Illini Hall
Course Description:
- This course provides an introduction to modern techniques for statistical
analysis of complex and massive data. Examples of these are regression
and classification,
nonparametric function estimation, model selection, regularization,
dimensionality
reduction, and clustering analysis. Applications are discussed as well as
computation and theoretical foundations.
A tentative list of topics can be found here.
Grading Policy:
-
Grading for this class will be based upon homework
(50%) and final project
(50%). Both can be done individually or in a team of two students.
Prerequisites:
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- Knowledge of 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.
- Some experience with one of the following
programming languages: R, S-plus or MATLAB.
Text:
- The main text for the course is Hastie, Tibshirani, and
Friedman's "The
Elements of Statistical Learning," but it will be supplemented by lecture
notes and current articles.