STAT 37710: Syllabus

A tentative one:

1. Week 1: The statistical learning framework, bias-variance tradeoffs

2. Week 2: Point estimation (MOME, MLE, MAP)

3. Week 3: Model complexity

4. Week 4: Classification (logistic regression; Naive Bayes, LDA)

5. Week 5: Ensemble methods (bagging, random forests, boosting)

6. Week 6: Graphical models (mixtures of Gaussians, graphical models)

7. Week 7: Multi-layer perceptrons and neural networks

8. Week 8: Nonparametric models (Kernel ridge regression, Gaussian processes)

9. Week 9: Support vector machines; active learning