| Date | Description | Chapters | Slides | Comments |
|---|
| Aug 28 | Lecture 1
Overview/Introduction
| 1, 2.1 |
lecture1a.pdf | |
| Aug 30 | Lecture 2
Hypothesis spaces |
2.7-2.10 |
lecture1b.pdf | |
| Sep 4 | Lecture 3
Space of Algorithms
Linear Threshold Classifiers |
2.7-2.10
10.1-10.4 |
lecture2a.pdf | |
| Sep 6 | Lecture 4
Project details
Perceptrons
Logistic Regressipon
| 11.1-11.4
10.6-10.7 |
lecture2b.pdf | HW 1 given out |
| Sep 11 | Lecture 5
Linear Discriminant Analysis |
6.6
5.1-5.5 |
lecture3a.pdf | |
| Sep 13 | Lecture 6
Off-The-Shelf Learning Algorithms
Decision Trees |
9.1-9.3 |
lecture3b.pdf | |
| Sep 18 | Lecture 7
Decision Trees (cont)
| |
lecture4a.pdf | HW 1 due
HW 2 given out |
| Sep 20 | Lecture 8
Nearest Neighbor |
8.1-8.5 |
lecture4b.pdf | Project pre-proposals due (1 page) |
| Sep 25 | Lecture 9
Neural networks |
11.5-11.7 |
lecture5a.pdf | HW 3 given out |
| Sep 27 | Lecture 10
Neural networks (cont) |
|
lecture5b.pdf | HW 2 due |
| Oct 2 | Lecture 11
Bayesian Learning |
Appendix A |
lecture6a.pdf | |
| Oct 4 | Lecture 12
Bayesian Learning (cont) |
3.1-3.4, 3.7
4.4-4.51 |
lecture6b.pdf | |
| Oct 9 | Lecture 13
Support Vector Machines |
10.9 |
lecture7a.pdf | HW 3 due
Project proposals due |
| Oct 11 | Lecture 14
Support Vector Machines (cont) |
|
lecture7b.pdf | |
| Oct 16 | MIDTERM |
|
| |
| Oct 18 | Lecture 15
Learning Theory |
2.2-2.3 |
learning-theory.pdf | |
| Oct 23 | NO CLASS - MAKEUP CLASS ON OCT 26 |
|
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| Oct 25 | NO CLASS - (tentative makeup class on Nov 9) |
|
| |
Oct 26
(make up class)
3:30pm-4:50pm
GFS 116 | Lecture 16
Learning Theory (cont) |
|
| |
| Oct 30 | Lecture 17
Learning Theory finished
Bias/Variance Theory & Ensemble Methods |
4.3
15.1-15.5 |
bias-variance.pdf | HW 4 |
| Nov 1 | Lecture 18
Bias/Variance Theory & Ensemble Methods (cont) |
|
| |
| Nov 6 | Lecture 19
Bias/Variance Theory & Ensemble Methods finished
Preventing Overfitting: Penalty and Hold-out methods |
4.7-4.9 |
overfitting.pdf | |
| Nov 8 | Lecture 20
Overfitting finished
Hold-Out and Cross-validation Methods
| 14.1-14.2 |
| HW 4 due
HW 5
adult-train-small.arff |
Nov 9 (make up class)
3:30-4:50pm
GFS 101 | Lecture 21
Penalty methods: decision trees, neural nets, SVMs |
8.6-8.8 |
penalty-methods.pdf | |
| Nov 13 | Lecture 22
Evaluating and Comparing Classifiers |
14.3-14.9 |
evaluation-of-classifiers.pdf | |
| Nov 15 | Lecture 23
Unsupervised Learning |
7 |
unsupervised-learning.pdf | |
| Nov 20 | Lecture 24
Unsupervised Learning (cont) |
7 |
| HW 5 due |
| Nov 22 | Thanksgiving - No class |
|
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| Nov 27 | No class - saved for presentations (Dec 4) |
|
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| Nov 29 | No class - saved for presentations (Dec 6) |
|
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Dec 4
3:30-4:50pm GFS 116
5:00-6:20pm GFS 118 | Lecture 25-26
Project Presentations 1 (double class)
| |
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Dec 6
3:30-4:50pm GFS 116
5:00-6:20pm GFS 118 | Lecture 27-28
Project Presentations 2 (double class)
| |
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| Dec 13 | FINAL: 7-9pm |
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