CS489/698 - Schedule


This is a tentative schedule only.  As the course progresses, the schedule will be adjusted.

[M] Tom Mitchell, Machine Learning (1997)
[BDSS] Shai Ben-David & Shai Shalev-Shwartz, Machine Learning: From Theoretical Priciples to Practical Algorithms (under writing)
[B] Christopher Bishop, Pattern Recognition and Machine Learning (2006)

Lecture
 Date
 Topic
 Reading (textbook)
 Notes
1
 Jan 5
 Introduction to Machine Learning (Slides 1/page 6/page)
 [M] Chapter 1
2
 Jan 7
 Decision trees and k-nearest neighbors
 [M] Chapter 3
3
 Jan 12
 Empirical risk minimization
 [BDSS] Lecture notes 2
4
 Jan 14
 Bias-Complexity Tradeoff
 [BDSS] Lecture notes 3
5
 Jan 19
 Minimum description length
 [BDSS] Lecture 4
6
 Jan 21
 Decision trees
 [BDSS] Lecture notes 5
 A1 out
7
 Jan 26
 No Free lunch
 [BDSS] Lecture notes 6
8
 Jan 28
 VC dimension
 [M] Chapter 7
[BSDD] Lecture notes 6
9
 Feb 2
 Linear regression
 [B] Sections 3.1.1, 3.1.2, 3.1.4, 3.1.5
[BSDD] Lecture notes 11
10
 Feb 4
 Linear regression by maximum likelihood (slides 1/page 6/page)
 [B] Section 3.2
11
 Feb 9
 Bayesian linear regression [B] Sections 3.3.1, 3.3.2
 A1 due
12
 Feb 11
 Linear models for classification [B] Section 4.1.7
[BSDD] Lecture notes 8
 A2 out
13
 Feb 23
 Linear models for classification [B] Sections 4.2.1, 4.2.2
14
 Feb 25
 Linear models for classification [B] Chapter 4.3.1, 4.3.2, 4.3.3, 4.3.4
 A2 due
15
 Mar 2
 Neural networks [M] Chapter 4
[B] Sections 5.1, 5.2
 A3 out
16
 Mar 4
 Neural networks [M] Chapter 4
[B] Sections 5.3.1, 5.3.2, 5.5.1, 5.5.2
17
 Mar 9
 Kernel methods [B] Section 6.1
18
 Mar 11
 Kernel methods [B] Section 6.2
19
 Mar 16
 Kernel methods
 [B] Section 6.4.1 6.4.2
 A3 due
20
 Mar 18
 Support Vector Machines [B] Section 7.1 (before 7.1.1)
 A4 out
21
 Mar 23
 Support Vector Machines [B] Section 7.1.1
22
 Mar 25
 Support Vector Machines [B] Section 7.1.1
23
 Mar 30
 Ensemble learning (slides 1/page 6/page) [B] Sections 14.2, 14.3.2
24
 Apr 1
 Course review
A4 due