| Date | Description | Chapters | Slides | Comments |
|---|
| Aug 26 (NO CLASS) |
NO CLASS - Saved for presentations (Dec 2) |
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| Aug 28 |
Lecture 1
Overview/Introduction
| 1, 2.1 |
01-intro.pdf |
|
| Sep 2 |
Lecture 2
Hypothesis spaces |
2.7-2.10 |
02-hypothesis_spaces.pdf |
|
| Sep 4 |
Lecture 3
Space of Algorithms
Linear Threshold Classifiers |
2.7-2.10
10.1-10.4 |
03-space_of_algs_and_LTUs.pdf |
|
| Sep 9 |
Lecture 4
Project details
Perceptrons
Logistic Regression
| 11.1-11.4
10.6-10.7 |
04-perceptrons_LogReg.pdf |
HW 1 |
| Sep 11 |
Lecture 5
Linear Discriminant Analysis |
6.6
5.1-5.5 |
05-LDA.pdf |
|
| Sep 16 |
Lecture 6
Off-The-Shelf Learning Algorithms
Decision Trees |
9.1-9.3 |
06-Off-The-Shelf-Algs-Decision-Trees.pdf |
|
| Sep 18 |
Lecture 7
Decision Trees (cont)
| |
07-Decision-Trees.pdf |
HW 1 due
HW 2 posted |
| Sep 23 |
Lecture 8
Nearest Neighbor |
8.1-8.5 |
08-Nearest-Neighbor.pdf |
Project pre-proposals due (1 page) |
| Sep 25 |
Lecture 9
Neural networks |
11.5-11.7 |
09-10-Neural-Networks.pdf |
|
| Sep 30 |
Lecture 10
Neural networks (cont) |
|
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HW 2 due
HW 3 posted |
| Oct 2 (NO CLASS) |
No class - saved for presentations (Dec 4) |
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| Oct 7 |
Lecture 11
Bayesian Learning |
Appendix A |
11-12-Bayesian-Learning.pdf |
|
| Oct 9 |
Lecture 12
Bayesian Learning (cont) |
3.1-3.4, 3.7
4.4-4.51 |
|
Project proposals due |
| Oct 14 |
Lecture 13
Support Vector Machines |
10.9 |
13-14-SVM.pdf |
HW 3 due |
| Oct 16 |
Lecture 14
Support Vector Machines (cont) |
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| Oct 21 |
MIDTERM |
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| Oct 23 |
Lecture 15
Learning Theory |
2.2-2.3 |
15-16-learning-theory.pdf |
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| Oct 28 |
Lecture 16
Learning Theory (cont) |
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| Oct 30 |
Lecture 17
Learning Theory finished
Bias/Variance Theory & Ensemble Methods |
4.3
15.1-15.5 |
17-18-bias-variance.pdf |
HW 4 posted |
| Nov 4 |
Lecture 18
Bias/Variance Theory & Ensemble Methods (cont) |
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| Nov 6 |
Lecture 19
Bias/Variance Theory & Ensemble Methods finished
Preventing Overfitting: Penalty and Hold-out methods |
4.7-4.9 |
19-20-overfitting.pdf |
|
| Nov 11 |
Lecture 20
Overfitting finished
Hold-Out and Cross-validation Methods
| 14.1-14.2 |
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HW 4 due |
| Nov 13 |
Lecture 21
Penalty methods: decision trees, neural nets, SVMs |
8.6-8.8 |
21-penalty-methods.pdf |
HW 5 posted
statlog.arff
statlog_test.arff
|
| Nov 18 |
Lecture 22
Evaluating and Comparing Classifiers |
14.3-14.9 |
22-evaluation-of-classifiers.pdf |
|
| Nov 20 |
Lecture 23
Unsupervised Learning |
7 |
23-24-unsupervised learning.pdf |
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| Nov 25 |
Lecture 24
Unsupervised Learning (cont) |
7 |
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HW 5 due |
| Nov 27 | Thanksgiving - No class |
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Dec 2 (double class)
3:30-4:50pm GFS 118
5:00-6:20pm GFS 118 |
Lecture 25-26
Project Presentations 1 (double class)
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Project papers due |
Dec 4 (double class)
3:30-4:50pm GFS 118
5:00-6:20pm GFS 118 |
Lecture 27-28
Project Presentations 2 (double class)
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| Dec 11 (GFS 118) |
FINAL: 4:30pm-6:30pm |
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