Subject to change.
| Date | Topics | Readings | Slides |
|---|---|---|---|
| M Aug 29 | Class Introduction & Reviews | math4ml | 01 |
| W Aug 31 | Reviews (Continue) | math4ml / syllabus | 02 |
| W Sep 7 | Introduction to ML & Decision Trees | CML 1 | 03 |
| M Sep 12 | Decision Trees (Continue) | CML 1 | 04 |
| W Sep 14 | Decision Trees and Limits of Learning | CML 2 | 05 |
| M Sep 19 | Geometry and Nearest Neighbors | CML 3-3.3 | 06 |
| W Sep 21 | K - Means Clustering (Unsupervised) | CML 3.4-3.5 | 07 |
| M Sep 26 | The Perceptron | CML 4-4.5 / NumPy for MATLAB Users | 08 |
| W Sep 28 | The Perceptron (continued) | CML 4.5-4.7 | 09 |
| M Oct 3 | Practical Issues | CML 5-5.5 | 10 |
| W Oct 5 | Imbalanced Data and Reductions | CML 6.1 | 11 |
| M Oct 10 | Multiclass Classifications and Reductions | CML 6.2-6.3 | 12 |
| W Oct 12 | Bias and Fairness | CML 8 | 13 |
| M Oct 17 | Binary Classification with Linear Models | CML 7-7.4 | 14 |
| W Oct 19 | Review and Practice Problems | 15 | |
| M Oct 24 | Midterm Exam | ||
| W Oct 26 | Break! | ||
| M Oct 31 | Gradient and Sub-Gradient Descent | CML 7.4-7.7 | 16 |
| W Nov 2 | Neural Networks 1 | CML 10-10.3 | 17 |
| M Nov 7 | Neural Networks 2 | CML 10.3-10.4 | 18 |
| W Nov 9 | Deep Learning 1 | 19 | |
| M Nov 14 | Deep Learning 2 | 20 | |
| W Nov 16 | SVMs 1 | CML 11.4-11.6 | 21 |
| M Nov 21 | SVMs 2 | CML 15-15.1 | 22 |
| W Nov 23 | Thanksgiving! | ||
| M Nov 28 | Kernel Methods | CML 11-11.3 | 23 |
| W Nov 30 | Probabilistic View of ML (Conditional Models) | CML 9-9.5 | 24 |
| M Dec 05 | Probabilistic View of ML 2 (Naive Bayes) | CML 9.6-9.7 | 25 |
| W Dec 07 | Unsupervised Learning (PCA) | CML 15.2 | 26 |
| M Dec 12 | Review and Perspective | Entire Course Review | |
| TBA | Final Exam |