| 1 | Introduction | 01/ |
| 2 | Probability: univariate models | 02/ |
| 3 | Probability: multivariate models | 03/ |
| 4 | Statistics | 04/ |
| 5 | Decision theory | 05/ |
| 6 | Information theory | 06/ |
| 7 | Linear algebra | 07/ |
| 8 | Optimization | 08/ |
| 9 | Linear discriminant analysis | 09/ |
| 10 | Logistic regression | 10/ |
| 11 | Linear regression | 11/ |
| 12 | Generalized linear models | 12/ |
| 13 | Neural networks for unstructured data | 13/ |
| 14 | Neural networks for images | 14/ |
| 15 | Neural networks for sequences | 15/ |
| 16 | Exemplar-based methods | 16/ |
| 17 | Kernel methods | 17/ |
| 18 | Trees | 18/ |
| 19 | Learning with fewer labeled examples | 19/ |
| 20 | Dimensionality reduction | 20/ |
| 21 | Clustering | 21/ |
| 22 | Recommender systems | 22/ |
| 23 | Graph embeddings | 23/ |
