| 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 |
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