Subject to change.
| Date | Topics | Readings | Slides |
|---|---|---|---|
| Tu Jan 25 | Class Introduction & Reviews | math4ml | 01 |
| Th Jan 27 | Reviews (Continue) | math4ml / syllabus | 02 |
| Tu Feb 1 | Introduction to ML & Decision Trees | CML 1 | 03 |
| Th Feb 3 | Decision Trees (Continue) | CML 1 | 04 |
| Tu Feb 8 | Decision Trees & Limits of Learning | CML 2 | 05 |
| Th Feb 10 | Geometry and Nearest Neighbors | CML 3-3.3 | 06 |
| Tu Feb 15 | K - Means Clustering (Unsupervised) | CML 3.4-3.5 | 07 |
| Th Feb 17 | The Perceptron | CML 4-4.5 / NumPy for MATLAB Users | 08 |
| Tu Feb 22 | The Perceptron (Continued) | CML 4.5-4.7 | 09 |
| Th Feb 24 | Practical Issues | CML 5-5.5 | 10 |
| Tu Mar 1 | Imbalanced Data & Reductions | CML 6.1 | 11 |
| Th Mar 3 | Multiclass Classification & Reductions | CML 6.2-6.3 | 12 |
| Tu Mar 8 | Bias & Fairness | CML 8 | 13 |
| Th Mar 10 | Binary Classification with Linear Models | CML 7-7.4 | 14 |
| Tu Mar 15 | Review & Pratice Problems | 15 | |
| Th Mar 17 | Midterm Exam | ||
| Spring Break! | |||
| Tu Mar 29 | Gradient & Sub-Gradient Descent | CML 7.4-7.7 | 16 |
| Th Mar 31 | Probabilistic View of ML (Conditional Models) | CML 9-9.5 | 17 |
| Tu Apr 5 | Probabilistic View of ML II (Naive Bayes) | CML 9.6-9.7 | 18 |
| Th Apr 7 | Unsupervised Learning (PCA) | CML 15.2 | 19 |
| Tu Apr 12 | Neural Networks I | CML 10-10.3 | 20 |
| Th Apr 14 | Neural Networks II | CML 10.3-10.4 | 21 |
| Tu Apr 19 | Deep Learning I | 22 | |
| Th Apr 21 | Deep Learning II | 23 | |
| Tu Apr 26 | Kernel Methods | CML 11-11.3 | 24 |
| Th Apr 28 | SVMs I | CML 11.4-11.6 | 25 |
| Tu May 3 | SVMs II | CML 15-15.1 | 26 |
| Th May 5 | Review & Perspective | Entire Course Review | 27 |
| Tu May 10 | TBD | ||
| Th May 12 | Final Exam |