DASC 5304 - Machine Learning
⭓DASC 5304 - Machine Learning
This is the course page for DASC 5304 - Machine Learning 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 | 12PM - 1PM | ERB 125 |
| TuTh | 1PM - 2PM | ERB 125 |
Teaching Assistant
Sudharani Bannengala (sxb7066 (at) mavs (dot) uta (dot) edu)
Office Hours
| Days | Time | Location |
|---|---|---|
| MoWe | 11AM - 12PM | ERB 501 |
⭓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.
Resources
⭓Schedule
This schedule is tentative and may change.
| Date | Topic | Materials |
|---|---|---|
| January 14 | Course Introduction | |
| January 16 | Supervised Learning | |
| January 21 | Probability Theory Review | |
| January 23 | Probabilistic Models | |
| January 28 | Kernels and Support Vector Machine | |
| January 30 | Using Python for Machine Learning, Decision Trees | |
| February 4 | Perceptron and Neural Networks | |
| February 6 | Ensemble Methods | |
| February 11 | Bagging, Random Forests, and Adaboost | |
| February 13 | Gradient Boosting | |
| February 18 | Intro. to Deep Learning | |
| February 20 | Convolutional Neural Networks | |
| February 27 | Optimization for Deep Learning | |
| March 4 | Automatic Differentiation | |
| March 6 | Recurrent Neural Networks, Transformers | |
| March 18 | Clustering | |
| March 20 | Principal Component Analysis | |
| March 25 | Segmentation by Clustering, GANs | |
| March 27 | Markov Decision Processes |