Lecture Plan and Progress
Topics covered:
- main parts of chapters 1-9
- possibly selected topics from the rest of the book
- asymptotic estimation theory (handouts, copies)
- empirical Bayes theory (handouts, copies)
Preliminary lecture plan and progress:
| Week | Chapter | Topic | Download | Remark |
|---|---|---|---|---|
| 3 | Ch. 1 | Introductory meeting | Introductory slides | Decision on lecture times |
| 4 | Ch. 2 | Overview of supervised learning | Slides week 4 | 4 hrs lecture (no exercises) |
| 5 | Ch. 3.1-3.4.1 | Linear methods for regression | Slides week 5 | |
| 6 | Rest of Ch. 3, except 3.7-3.8 | Linear methods for regression | Multivariate prediction and regression, Maximization result | |
| 7 | Ch. 4, except 4.4.3 and 4.5 | Linear methods for classification | Slides week 7 | 4.4.3 is covered in TMA4315 Generalized linear models |
| 8 | Ch. 5, except 5.8, 5.9 and Appendix | Basis expansions and regularization | Slides week 8 | Splines (and smoothing splines) are nicely covered in the book "Generalized Additive Models" by Hastie and Tibshirani |
| 9 | Ch. 6 | Kernel smoothing methods | Kernel density estimation | |
| 10 | Ch. 7.1-7.6 (7.7, 7.8, 7.9 and 7.12 are not in curriculum) | Model assessment and selection | Slides week 10 | |
| 11 | Ch. 7.10-7.11 and some remaining issues from 7.1-7.6. 8.1.8.2 | Model selection and inference | ||
| 12 | Trial exam | |||
| 13 | Easter vacation | |||
| 14 | Easter vacation | |||
| 15 | Ch. 8.5 (8.5.1 and 8.5.2) | Maximum likelihood estimation and asymptotic estimation theory | Bookchapter on large sample theory | Ch. 8.5.1-8.5.2 plus the book chapter will be taught. |
| 16 | Empirical Bayes theory | Casella: An introduction to empirical Bayes data analysis (article), Chapter on Stein's paradox | ||
| 17 | Ch. 1-8 | Discussion of relevant parts of all the curriculum |