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. 18.1-18.4, [HTF]
Sec. 9.2, [D] Chapt. 1, [B] Sec. 14.4, [M] Sec. 16.2 |
3 |
Jan 12 |
K-nearest 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. 3.1-3.3, [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 |
Multi-layer 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 12-18 corrected March 23)) | [RN] Sec. 15.3 [B] Sec.
13.1-13.2 [M] Sec. 17.3-17.5 |
18 |
Mar 10 |
Hidden Markov models continued (slides (slides 8-16 corrected March 23)) | [RN] Sec. 15.3 [B] Sec. 13.1-13.2 [M] Sec. 17.3-17.5 |
19 |
Mar 15 |
Deep neural networks (slides) | [GBC] Chap. 6-9 |
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. 15-16, [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. 15-16, [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) |