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 knearest neighbors 
[M] Chapter 3 

3 
Jan 12 
Empirical risk minimization 
[BDSS] Lecture notes 2 

4 
Jan 14 
BiasComplexity 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 