STAT 4250/6250 Fall 2026
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, homework assignments, 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 9:55 AM - 11:15 AM
- Caldwell Hall, Room 202
Teaching team and office hours #
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Instructor: Xiaotian Zheng
Office Hours: Thursday 11:30 AM - 12:30 PM (or by appointment) at Brooks Hall 452
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Teaching Assistant: Kyle Fuentes-Keuthan
Office Hours: Friday 1:30 PM - 2:30 PM at Brooks Hall 509
Final project #
The final project is a group project. Project guidelines will be posted on eLC.
Key dates #
TBD
Lecture schedule #
The schedule will be updated as the course progresses.
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 1 | Aug. 18 | Introduction | AMSA Ch. 2.2-2.3 |
| Lecture 2 | Week 1 | Aug. 20 | Data Visualization | AMSA Ch. 1.3-1.4 |
| Lecture 3 | Week 2 | Aug. 25 | Descriptive Statistics | AMSA Ch. 1.3, 2.5-2.6 |
| Lecture 3 | Week 2 | Aug. 27 | Descriptive Statistics | AMSA Ch. 3.3-3.4 |
| Lecture 3-4 | Week 3 | Sep. 01 | Descriptive Statistics and multivariate Gaussian distributions | AMSA Ch. 3.6, 4.1-4.2 |
| Lecture 5 | Week 3 | Sep. 03 | Linear regression I | AMSA Ch. 7.1-7.3 |
| Lecture 6 | Week 4 | Sep. 08 | Linear regression II | ISL Ch. 3.1-3.2, 5.1, 6.1 |
| Lecture 6-7 | Week 4 | Sep. 10 | Linear regression II & III | |
| Lecture 7 | Week 5 | Sep. 15 | Linear regression III | ISL Ch. 2.2, 6.2 |
| Lecture 8 | Week 5 | Sep. 17 | Principal Component Analysis | AMSA Ch. 8.1 - 8.3 |
| Lecture 8 | Week 6 | Sep. 22 | Principal Component Analysis | |
| Week 6 | Sep. 24 | Review | ||
| Week 7 | Sep. 29 | Exam 1 | ||
| Lecture 9 | Week 7 | Oct. 01 | Factor Analysis | AMSA Ch. 9.1 - 9.2 |
Acknowledgements #
Course materials were adapted from the same course previously offered by Associate Professors Yuan Ke and Ruizhi Zhang at UGA, and drew on materials and examples from Goodfellow et tal. (2017), Hamilton (2020), Hastie et al. (2009), Johnson and Wichern (2007), James et al. (2021), and Nielsen (2015).
References #
Goodfellow, I., Bengio, Y., & Courville, A. (2017) Deep Learning. Cambridge, MA: MIT Press.
Hamilton, W. L. (2020). Graph Representation Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 14, No. 3 , Pages 1-159.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). New York, NY: Springer.
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.
Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press.