Lecture Plan and Progress

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Week	Chapter	Topic	Download	Remark
2	Ch. 1	Introductory meeting		Decision on lecture times
3	Ch. 2	Overview of supervised learning	Slides Ch. 2	4 hrs lecture (no exercises)
4	Ch. 3.1-3.4	Linear methods for regression	Slides Ch. 3, Maximization result
5	Rest of Ch. 3, except 3.7-3.8	Linear methods for regression	Multivariate prediction and regression
6	Ch. 4, except 4.4.3 and 4.5	Linear methods for classification	Slides Ch. 4
7	Ch. 5, except 5.8, 5.9 and Appendix	Basis expansions and regularization	Slides Ch. 5
8	Ch. 6	Kernel smoothing methods	Kernel density estimation	No lecture on Thursday 23 February. Trial exam.
9	Ch. 7 (7.7, 7.8, 7.9 and 7.12 are not in curriculum)	Model assessment and selection	Slides Ch. 7
10	Ch. 7.10-7.11 and some remaining issues from 7.1-7.6. 8.1, 8.2, 8.7.	Model selection and inference
11	Ch. 9.1 (not all details), 9.2. INTRODUCTION: Ch. 8.1, 8.2.1, 8.2.2	Additive models, trees, and related methods (bagging, random forests)	Bookchapter (B. Ripley: Pattern Recognition and Neural Networks), Example: Pruning and cross-validation	Additional reading: Ch. 3 in "Berk: Statistical Learning".
12	Ch. 12.1, 12.2, (skim 12.3). INTRODUCTION: Ch. 9.1-9.3	Support Vector Machines
13	Ch. 10.1-10.5. INTRODUCTION: Ch. 8.2.3	Boosting and additive trees		Additional reading: Ch. 6 in "Berk: Statistical Learning".
14	Ch. 11	Neural Networks		Additional reading: Ch. 8.1 in "Berk: Statistical Learning".
15	Easter vacation
16	Final meeting on Thursday April 20	Summing-up		No lecture on Tuesday 18 April (NTNU no teaching day)