CSE 6363 - Machine Learning (8-week)
⭓Description
This is the course page for CSE 6363 - Machine Learning (8-week) in Spring 2025. Here you’ll find class notes and other helpful resources. All assignments and announcements will be posted on Canvas.
Instructor Office Hours
| Days | Time | Location |
|---|---|---|
| MoWeFri | 10AM - 11AM | ERB 556A |
| TuTh | 11AM - 12PM | ERB 556A |
Teaching Assistant
TBD
⭓Course Materials
Books
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An Introduction to Statistical Learning, Springer, 2023.
- Kevin Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022.
Papers
Resources
⭓Schedule
This schedule is tentative and may change.
| Date | Topic | Materials |
|---|---|---|
| August 18 | Course Introduction, Supervised Learning | |
| August 20 | Regularization, Probability Theory, and Linear Discriminant Analysis | |
| August 25 | Naive Bayes Classifier, Support Vector Machine | |
| August 27 | Support Vector Machine, Neural Networks | |
| September 3 | Decision Trees, Boosting and Bagging | |
| September 8 | Gradient Boosting, Deep Learning | |
| September 10 | CNNs for Object Detection, Python for Deep Learning |