These lecture notes occasionally refer to slides, such as at the end of lectures 5 and 7. These slides are not available on MIT OpenCourseWare.
| LEC # | TOPICS |
|---|---|
| 1 | Rule mining and the Apriori algorithm (PDF) |
| 2 | R for machine learning (PDF) (Courtesy of Allison Chang. Used with permission.) |
| 3 | Fundamentals of learning (PDF) |
| 4 | Inference (PDF) |
| 5 | Clustering (PDF) |
| 6 | k-nearest neighbors (PDF) |
| 7 | Naïve Bayes (PDF) |
| 8 | Decision trees (PDF) |
| 9 | Logistic regression (PDF) |
| 10 | Boosting (PDF) |
| 11 | Convex optimization (PDF) |
| 12 | Support vector machines (PDF) |
| 13 | Kernels (PDF) |
| 14 | Statistical learning theory (PDF) |
| 15 | Bayesian analysis (PDF - 1.2MB) (Courtesy of Ben Letham. Used with permission.) |
