ST 697: Statistical Learning

Fall 2017



Schedule


Class Date Day Topic Reading (Pre-Class) Notes from class
1 Aug 24 Thu Intro Chap 1 Streak Betting
2 Aug 29 Tue Supervised Learning 2.1-2.3 02-supervised.pdf handout
3 Aug 31 Thu Supervised Learning 2.4-2.6
4 Sept 5 Tue Supervised Learning 2.7-2.9 02-bias-variance.pdf handout
5 Sept 7 Thu Linear Regression 3.1-3.2 HW #1 Due R Formula Interface
6 Sept 12 Tue Subset Selection 3.3 R Code: subsets
7 Sept 14 Thu Shrinkage Methods 3.4 R Code: ridge
8 Sept 19 Tue Lasso 3.6 R Code: lasso
9 Sept 21 Thu Algorithms 3.8-3.9
10 Sept 26 Tue HW #2 Due
11 Sept 28 Thu Logistic Regression 4.1-4.2; 4.4
12 Oct 3 Tue GLM Rodriguez Notes
13 Oct 5 Thu Model Assessment 7.1-7.3; 7.10
14 Oct 10 Tue Model Assessment 7.4-7.7; 7.11
15 Oct 12 Thu Penalized B splines 5.1-5.2; Eilers & Marx R Code: B-spline
16 Oct 17 Tue Generalize Additive Models 5.6, 9.1
17 Oct 19 Thu Classification/Pattern Recognition HW #3 Due
18 Oct 24 Tue LDA 4.3
Oct 26 Thu No Class: Fall Break
19 Oct 31 Tue Nonparametric Density Estimation 6.6-6.8
20 Nov 2 Thu Naive Bayes; Kernel Smoothing 6.1-6.5
21 Nov 7 Tue Bayesian Estimation and Bootstrap 8.1-8.4 HW #4 Due
22 Nov 9 Thu Trees 9.2, 8.7-8.9
23 Nov 14 Tue Ensembles & Random Forests 8.7-8.9, 15
24 Nov 16 Thu Boosting I 10; LogitBoost paper
25 Nov 21 Tue Boosting II 16
Nov 23 Thu No Class: Thanksgiving Break
26 Nov 28 Tue Anomaly detection I
27 Nov 30 Thu Anomaly detection II
28 Dec 5 Tue Networks and Pagerank
29 Dec 7 Thu Open Topics