Time: Monday, Wednesday, Friday at 9:00 AM - 9:50 AM
Location (in person and on campus): Lecture Center Building A (805 S Morgan St, Chicago, IL 60607), Room A002
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 11:00 a.m. - 12:00 p.m. at UIC Zoom or by appointment
Textbook (required):
Trevor Hastie, Robert Tibshirani, Jerome Friedman,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, 2017.
Reference Books (optional):
Attendance/Participation Policy:
Students are expected to attend the lectures and participate in the discussions. Attendance will be counted at least six times during the course period and the students who present may receive half credit point each time and up to three extra credit points in total on the final grade at a 100-point scale. Students who actively participate in the discussion may receive half or one credit point each time and up to five credit points in total. If for any reason you could not present in class on time, please send me an email at your earliest convenience. If you need special accommodations due to disabilities, please contact the Disability Resource Center for a Letter of Accommodation (LOA).
Assignments, Due Dates, and Deadlines:
Homework will be assigned at a weekly basis;
turn in your homework every Wednesday before class via UIC Blackboard;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem.
Policy for Missed or Late Homework: Students may request up to two days' extension for each homework; late homework without request ahead of time or longer than two days will not be accepted; the lowest one homework score will be dropped for final grade.
Exam: March 7th, 2025 (Friday), 9:00 a.m. - 9:50 a.m.
Project: Students are required to work in groups on course projects and submit their final reports before May 2nd, 2025, Friday, 9:00am.
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 30%, Exam 30%, Project 40%
Grading Scale: 90% A , 80% B , 70% C , 60% D
Format of Exam: The exam is based on the homework and the examples discussed in class. The last class session before the 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 |
| 01/13 - 01/17 | Chapter 1; 3.2; 3.2 | Introduction to Statistical Learning; Linear Regression Models and Least Squares |
| 01/20 - 01/24 | Holiday; 3.3; 3.3 | Subset Selection |
| 01/27 - 01/31 | 3.4; 3.4; 3.4 | Ridge Regression; Lasso; Least Angle Regression |
| 02/03 - 02/07 | 3.5; 3.5; 3.8 | Principal Components Regression; Partial Least Squares; Grouped Lasso |
| 02/10 - 02/14 | 7.5; 7.7; 7.10 | AIC; BIC; Cross-Validation |
| 02/17 - 02/21 | Sampling: Chapter 1; Chapter 2; Chapter 3 | Introduction to Sampling; Simple Random Sampling; Stratified Sampling |
| 02/24 - 02/28 | Sampling: Chapter 5; Sampling: Lecture Notes | Cluster Sampling; Subsampling Techniques for Big Data Analysis |
| 03/03 - 03/07 | Sampling: Lecture Notes; Review; Exam | Subsampling Techniques for Big Data Analysis |
| 03/10 - 03/14 | NSI: 3.2; 3.3; 3.4, 3.5 | Nonparametric Statistical Inference: Tests of Randomness |
| 03/17 - 03/21 | NSI: 4.2; 4.3; 4.5, 4.6 | Nonparametric Statistical Inference: Tests of Goodness of Fit |
| 03/31 - 04/04 | 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 |
| 04/07 - 04/11 | 4.1; 4.3; 4.3 | Introduction for Classification; Linear Discriminant Analysis |
| 04/14 - 04/18 | 4.4; 4.4; 4.4 | Logistic Regression |
| 04/21 - 04/25 | 11.3; 11.3; 12.2 | Neural Networks; Support Vector Classifier |
| 04/28 - 05/02 | 12.3; 13.3; 14.3 | Support Vector Machines and Kernels; k-Nearest-Neighbor Classifiers; K-means |