STAT 4250/6250 Fall 2025
Applied Multivariate Analysis and Statistical Learning
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eLearning Commons #
UGA’s eLearning Commons (eLC) is the university’s learning management system. All class communications, announcements, syllabus, homework assignments, lecture slides, and other materials will be posted exclusively on eLC. Students are responsible to check eLC regularly for updates on course requirements and deadlines. This website is intended only as a supplementary resource.
Lecture time and location #
- Tuesday and Thursday 2:20 PM - 3:35 PM
- Caldwell Hall, Room 204
Teaching team and office hours #
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Instructor: Xiaotian Zheng
Office Hour: Firday, 10:00 AM - 11:00 AM
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Teaching Assistant: Diqing Guo
Office Hours: Monday, 1:30 PM - 2:30 PM
Final project #
The final project guidelines have been posted on eLC.
Key dates #
- Sep. 23: Exam 1.
- Oct. 21: Exam 2.
- Oct. 30: Project proposal due.
- Nov. 21: Project report due.
- Nov. 25: Project presentation.
Lecture schedule #
The schedule is subject to change.
AMSA = Applied Multivariate Statistical Analysis (6th ed.)
[link]
ISL = An Introduction to Statistical Learning with Applications in R (2nd ed.)
[link]
| Week | Date | Topic | Readings (optional) | |
|---|---|---|---|---|
| Lecture 1 | Week 0 | Aug. 14 | Introduction | AMSA Ch. 2.2-2.3 |
| Lecture 2 | Week 1 | Aug. 19 | Data Visualization | AMSA Ch. 1.3-1.4 |
| Lecture 3 | Week 1 | Aug. 21 | Descriptive Statistics | AMSA Ch. 1.3, 2.5-2.6, 3.3-3.4 |
| Lecture 4 | Week 2 | Aug. 26 | Linear Regression I | AMSA Ch. 7.1-7.3 |
| Lecture 4 | Week 2 | Aug. 28 | Linear Regression I | ISL Ch. 3.1-3.2, 5.1, 6.1 |
| Lecture 5 | Week 3 | Sep. 2 | Linear Regression II (ridge regression and the lasso) | ISL Ch. 2.2, 6.2 |
| Lecture 6 | Week 3 | Sep. 4 | Linear Regression III (variable selection and regularization methods) | - |
| Lecture 7 | Week 4 | Sep. 9 | Principal Component Analysis | AMSA Ch. 8.1 - 8.3 |
| Lecture 7 | Week 4 | Sep. 11 | Principal Component Analysis | - |
| Lecture 8 | Week 5 | Sep. 16 | Principal Component Regression | ISL Ch. 6.3 |
| - | Week 5 | Sep. 18 | Review | - |
| - | Week 6 | Sep. 23 | Exam 1 | - |
| Lecture 9 | Week 6 | Sep. 25 | Factor Analysis | AMSA Ch. 9.1 - 9.2 |
| Lecture 9 | Week 7 | Sep. 30 | Factor Analysis | AMSA Ch. 9.3 - 9.5 |
| Lecture 10 | Week 7 | Oct. 2 | Linear Discriminant Analysis | ISL Ch. 4.4 |
| Lecture 10 | Week 8 | Oct. 7 | Linear Discriminant Analysis | AMSA Ch. 11.5 - 11.6 |
| Lecture 11 | Week 8 | Oct. 9 | Support Vector Machine I | ISL Ch. 9.1 |
| Lecture 12 | Week 9 | Oct. 14 | Support Vector Machine II | ISL CH. 9.2 - 9.3 |
| - | Week 9 | Oct. 16 | Review | - |
| - | Week 10 | Oct. 21 | Exam 2 | - |
| Lecture 13 | Week 10 | Oct. 23 | Clustering I | - |
| Lecture 14 | Week 11 | Oct. 28 | Clustering II | - |
| Lecture 15 | Week 11 | Oct. 30 | Tree-based methods I | - |
| Lecture 16 | Week 12 | Nov. 4 | Tree-based methods II | - |
| Lecture 17 | Week 12 | Nov. 6 | Inference on multivariate means and analysis of multivariate variances | - |
| Lecture 18 | Week 13 | Nov. 11 | Introduction to neural networks | - |
| Lecture 19 | Week 13 | Nov. 13 | Convolutional neural networks | - |
| Lecture 20 | Week 14 | Nov. 18 | Graph neural networks | - |
| Lecture 21 | Week 14 | Nov. 20 | Autoencoders and representation learning | - |
| - | Week 15 | Nov. 25 | Final Project Presentation | - |
Acknowledgements #
Course materials are adapted from the same course previously offered by Associate Professors Yuan Ke and Ruizhi Zhang at UGA, and draw on materials and examples from Johnson and Wichern (2007), James et al. (2021), and Hastie et al. (2009).
References #
Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Upper Saddle River, NJ: Pearson Education.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). New York, NY: Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). New York, NY: Springer.