Lecture 
Date 
Topic 
Reading
(textbook) 
Notes 
1 
Jan 3 
Introduction to Machine Learning
(Lecture slides) 
[B] Chapter 1 

2 
Jan 5 
Decision
trees (Lecture slides) 
[B] Section 14.4 

3 
Jan 10 
Nearest neighbour and
statistical learning (Lecture slides) 
[B] Section 2.5.2 

4 
Jan 12 
Linear Regression (Lecture slides) 
[B] Section 3.1 

5 
Jan 17 
Linear Regression (Lecture
slides  revised Feb 10) 
[B] Sections 3.13.3 
A1 out 
6 
Jan 19  Linear models for classification
(Lecture slides  revised Jan 30) 
[B] Sections 4.2, 4.3  
7 
Jan 24 
Perceptrons, neural networks (Lecture slides) 
[B] Sections 4.1.7, 5.1 

8 
Jan 26 
Multilayer Neural networks,
Backpropagation (Lecture slides) 
[B] Sections 5.2, 5.3  
9 
Jan 31 
Kernel methods (Lecture slides) 
[B] Sections 6.1, 6.2 

10 
Feb 2 
Gaussian Processes (Lecture slides) 
[B] Sections 6.4.1, 6.4.2 
A1 due A2 out 
11 
Feb 7 
Support vector machines (Lecture slides) 
[B] Section 7.1 (before Section
7.1.1) 

12 
Feb 9 
Support vector machines (Lecture slides) 
[B] Section 7.1.1 

13 
Feb 14 
Hidden Markov models (Lecture slides) 
[B] Sections 13.1, 13.2 

14 
Feb 16 
Hidden Markov models (Lecture slides) 
[B] Section 13.1, 13.2 

15 
Feb 28 
PAC (Probably Approximately
Correct) Learning (Lecture slides) 
[BDSS] Chapters 1 
A2 due 
16 
Mar 1 
Learning via Uniform
Convergence
(Lecture slides) 
[BDSS] Chapter 2, 3 
A3 out Project proposal due 
17 
Mar 6 
Nofreelunch theorem,
biascomplexity tradeoff (Lecture
slides) 
[BDSS] Chapter 4 

18 
Mar 8 
VC (VapnikChervonenkis)
dimension (Lecture slides) 
[BDSS] Chapter 5 

19 
Mar 13 
Hypothesis dependent bounds (Lecture slides) 
[BDSS] Chapters 67 

20 
Mar 15 
No new material 
A3 due A4 out 

21 
Mar 20 
Nearest neighbor analysis (Lecture slides) 
[BDSS] Chapter 9  
22 
Mar 22 
Lecture cancelled due to
overheated classroom 


23 
Mar 27 
Computational Complexity and
Validation (Lecture slides) 
[BDSS] Chapter 10 

24 
Mar 29 
course wrap up (Lecture slides) 
A4 due 