Time: Monday, Wednesday, Friday at 10:00 AM - 10:50 AM
Location: Taft Hall 219
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT math DOT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 11:00 a.m. - 12:00 p.m.
Textbook:
Trevor Hastie, Robert Tibshirani, Jerome Friedman,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, 2009.
Reference Books:
Homework:
Turn in every Wednesday before class;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem.
Exams: October 12th (Friday), and November 16th (Friday), 10:00 a.m. - 10:50 a.m.
Project: Students are required to work in groups on course projects and submit their final reports before December 7th, Friday, 10:00 a.m..
Each group should consist of at most three students. The projects may come from the optional problems assigned
by the instructor or be proposed by the students themselves upon the approval of the instructor.
Grading: Homework 20%, Two Exams 25% each, Project 30%
Grading Scale: 90% A , 80% B , 70% C , 60% D
Format of All Exams: Exams are mainly based on the homework and the examples discussed in class. The last class session before each exam is a review session. Please prepare any questions that you may have.
No makeup exam will be given without a valid excuse.
| WEEK | SECTIONS | BRIEF DESCRIPTION |
| 08/27 - 08/31 | Chapter 1; 3.2; 3.2 | Introduction to Statistical Learning; Linear Regression Models and Least Squares |
| 09/03 - 09/07 | Holiday; 3.4; 3.4 | Ridge Regression; Lasso |
| 09/10 - 09/14 | 3.4; 3.5; 3.5 | Least Angle Regression; Principal Components Regression; Partial Least Squares |
| 09/17 - 09/21 | 7.5; 7.7; 7.10 | AIC; BIC; Cross-Validation |
| 09/24 - 09/28 | Sampling: Chapter 1; Chapter 2; Chapter 2 | Introduction to Sampling; Simple Random Sampling |
| 10/01 - 10/05 | Sampling: Chapter 3; Chapter 3; Chapter 5 | Stratified Sampling; Cluster Sampling |
| 10/08 - 10/12 | Sampling: Chapter 5; Review; Exam-1 | Cluster Sampling |
| 10/15 - 10/19 | NSI: 3.2; 3.3; 3.4, 3.5 | Nonparametric Statistical Inference: Tests of Randomness |
| 10/22 - 10/26 | NSI: 4.2; 4.3; 4.5, 4.6 | Nonparametric Statistical Inference: Tests of Goodness of Fit |
| 10/29 - 11/02 | NSI: 5.7; 6.3, 6.6; 8.2 | Nonparametric Statistical Inference: Wilcoxon Signed-Rank Test; Kolmogorov-Smirnov Two-Sample Test, Mann-Whitney U Test; Wilcoxon Rank-Sum Test |
| 11/05 - 11/09 | 4.1; 4.3; 4.3 | Introduction for Classification; Linear Discriminant Analysis |
| 11/12 - 11/16 | 4.4; Review; Exam-2 | Logistic Regression |
| 11/19 - 11/23 | 4.4; 11.3; Holiday | Logistic Regression; Neural Networks |
| 11/26 - 11/30 | 12.2; 12.3; 12.3 | Support Vector Classifier; Support Vector Machines and Kernels |
| 12/03 - 12/07 | 13.3; 14.3; 14.3 | k-Nearest-Neighbor Classifiers; Cluster Analysis; K-means |