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 |
(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.) |