Lecture 
Date 
Topic 
Readings
(textbooks) 
1 
Jan 5 
Introduction to Machine
Learning (slides) 

2 
Jan 7 
Decision trees (slides (slide 20
corrected Feb 10)) 
[RN] Sec. 18.118.4, [HTF]
Sec. 9.2, [D] Chapt. 1, [B] Sec. 14.4, [M] Sec. 16.2 
3 
Jan 12 
Knearest neighbours (slides (slide 6 corrected
Jan 27)) 
[RN] Sec. 18.8.1, [HTF] Sec.
2.3.2, [D] Chapt. 2, [B] Sec. 2.5.2, [M] Sec. 1.4.2 
4 
Jan 14 
Linear regression (slides) 
[RN] Sec. 18.6.1, [HTF] Sec.
2.3.1, [D] Sec. 6.6, [B] Sec. 3.1, [M] Sec. 1.4.5 
5 
Jan 19 
Statistical learning (slides)  [RN] Sec. 20.1, 20.2, [M]
Sec. 2.2, 3.2 
6 
Jan 21  Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (slides)  [B] Sec. 3.13.3, [M] Chap. 7 
Jan 25 
Assignment 1 due (11:59 pm) 

7 
Jan 26 
Mixture of Gaussians (slides (slide 5 corrected
Feb 1)) 
[B] Sec. 4.2, [M] Sec. 4.2 
8 
Jan 28 
Logistic regression,
generalized linear models (slides) 
[RN] Sec. 18.6.4, [B] Sec.
4.3, [M] Chap. 8, [HTF] Sec. 4.4 
9 
Feb 2 
Perceptrons, single layer
neural networks (slides) 
[D] Chapt. 3, [HTF] Chapt.
11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7 
10 
Feb 4 
Multilayer Neural networks,
Backpropagation (slides
(slide 6 corrected Feb 7, slide 14 corrected Feb 10))(Quick Recap) 
[D] Chapt. 8, [HTF] Chapt.
11, [B] Sec. 5.2, 5.3, [M] Sec. 16.5, [RN] Sec. 18.7 
Feb 8 
Assignment 2 due (11:59 pm) 

11 
Feb 9 
Guest lecture by Theophanis
Stratopoulos 
Two papers: 1) Emerging Technology Adoption and Expected Duration of Competitive Advantage 2) Financial Reports Based Proxies for Bargaining Power of Buyers and Sellers 
12 
Feb 11 
Midterm (in class) 

Feb 16 
Reading break (no class) 

Feb 18 
Reading break (no class) 

13 
Feb 23 
Kernel methods (slides (slides 7,8
corrected Feb 26)) 
[B] Sec. 6.1, 6.2 [M] Sec.
14.1, 14.2 [H] Chap. 9 [HTF] Chap. 6 
14 
Feb 25 
Gaussian Processes (slides (slide 11
corrected March 2)) 
[B] Sec. 6.4 [M] Chap. 15
[HTF] Sec. 8.3 
Feb 29 
Project proposal due (11:59 pm) 

15 
Mar 1 
Support vector machines (slides (slide 13
corrected March 8)) 
[B] Sec. 7.1 [D] Sec. 6.7
[HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
16 
Mar 3 
Support vector machines
continued (slides) 
[B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4 
Mar 7 
Assignment 3 due (11:59 pm) 

17 
Mar 8 
Hidden Markov models (slides (slides 1218 corrected March 23))  [RN] Sec. 15.3 [B] Sec.
13.113.2 [M] Sec. 17.317.5 
18 
Mar 10 
Hidden Markov models continued (slides (slides 816 corrected March 23))  [RN] Sec. 15.3 [B] Sec. 13.113.2 [M] Sec. 17.317.5 
19 
Mar 15 
Deep neural networks (slides)  [GBC] Chap. 69 
20 
Mar 17 
Convolutional neural
networks (slides) 
[GBC] Chap. 10 
Mar 23 
Assignment 4 due (11:59 pm) 

21 
Mar 22 
Recurrent and recursive
neural networks (slides) 
[GBC] Chap. 11 
22 
Mar 24 
Ensemble learning: bagging
and boosting (slides) 
[RN] Sec 18.10, [M] Sec.
16.2.5, [B] Chap. 14, [HTF] Chap. 1516, [D] Chap. 11 
23 
Mar 29 
Bagging, decision forests and
distributed computing (slides) 
[RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 1516, [D] Chap. 11 
24 
Mar 31 
Stream learning (slides) course wrap up 
[M] Sec. 8.5 
Apr 6 
Assignment 5 due (11:59 pm) 

Apr 18 
Project report due (11:59 pm) 