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Machine Learning
PhD course, 2013
Lectures
All lectures takes place in Signalen, for location, click here.
Each lecture comes with a list of recommended problems to be solved as shown in the table below. If there are no letters in front of the numbers, they refer to problems in the book by Bishop. If the letters HTF appears in front of the number that means that exercise is to be found in the book by Hastie, Tibshirani and Friedman.
Note that the slides provided below only covers a small part of the lectures, the whiteboard is
used quite extensively.
| Nr. | Date | Contents | Pres. |
|---|---|---|---|
| 1 | Jan. 16 (13-15) | Introduction (Chap. 1-2 and
notes) Problems: 2.13, 2.29, 2.32, 2.34, 2.40, 2.44, 2.47 |
pdf |
| 2 | Jan. 23 (13-15) | Linear regression (Chap. 3, HTF Chap. 3) Problems: 1.25, 1.26, 3.8, 3.9, 3.12, 3.13 |
pdf |
| 3 | Jan. 30 (13-15) | Linear classification (Chap. 4) Problems: 4.5, 4.19, 4.25, HTF:2.8 |
pdf |
| 4 | Feb. 6 (13-15) | Neural networks, kernel methods intro.
(Chap. 5-6.3) Problems: 5.4, 5.16, 6.3, HTF: 11.5 |
pdf |
| 5 | Feb. 13 (13-15) |
Kernel machines
(Chap. 6.4-7) Problems: Available here, m-file |
pdf |
| 6 | Feb. 25 (13-15) | EM and clustering (Notes
and Chap. 9) Problems: 9.8, 9.9, 9.11, 12.24 (also in Matlab, see lecture 1) |
pdf |
| 7 | Feb. 27 (13-15) | Approximate inference (
Notes, Code and Chap. 10) Problems: 10.4, 10.7, 10.26, 10.38 |
pdf |
| 8 | Mar. 6 (13-15) | Boosting, graphical model intro.
(Chap. 14.3, 8.1-8.2) Problems: 14.6, 14.7, 8.1, 8.3, 8.4, 8.7 |
pdf |
| 9 | Mar. 18 (10-12) | Graphical models (Chap. 8, code) Problems: 8.10, 8.11, 8.19, 8.23, 8.27 |
pdf |
| 10 | Mar. 20 (15-17) | MCMC and sampling methods
(Code and Chap. 11) Problems: Available here, m-file |
pdf |
| 11 | Mar. 22 (13-15) | Bayesian nonparametric models (P1, P2, P3 and Code) contributed by Fredrik Lindsten |
pdf |
Informationsansvarig: Thomas Schön
Senast uppdaterad: 2013-04-02
