CS489/698 - Schedule


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

[GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (2016) freely available online
[HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman, Elements of Statistical Learning (2nd edition, 2009) freely available online
[D] Hal Daume III, A Course in Machine Learning (in progress) freely available online
[B] Christopher Bishop, Pattern Recognition and Machine Learning (2006)
[RN] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition) (2010)
[M] Kevin Murphy, Machine Learning: A Probabilistic Perspective (2012)
[MRT] Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, Foundations of Machine Learning (2012)
[SSBD] Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms (2014)

Lecture
 Date
 Topic
 Readings (textbooks)
1
 Jan 4
 Introduction to Machine Learning (Lecture slides)
2
 Jan 9
 K-nearest neighbours (Lecture slides) [RN] Sec. 18.8.1, [HTF] Sec. 2.3.2, [D] Chapt. 3, [B] Sec. 2.5.2, [M] Sec. 1.4.2
3
 Jan 11
 Linear regression (Lecture slides) (Notation reference sheet)
 [RN] Sec. 18.6.1, [HTF] Sec. 2.3.1, [D] Sec. 7.6, [B] Sec. 3.1, [M] Sec. 1.4.5
4
 Jan 16
 Statistical learning (Lecture slides)
 [RN] Sec. 20.1, 20.2, [M] Sec. 2.2, 3.2
5
 Jan 18
 Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (Lecture slides)
 [B] Sec. 3.1-3.3, [M] Chap. 7

Jan 20
 Assignment 1 due (11:59 pm)
6
 Jan 23 Mixture of Gaussians (Lecture slides)
 [B] Sec. 4.2, [M] Sec. 4.2
7
 Jan 25
 Logistic regression, generalized linear models (Lecture slides (revised Jan 30)) [RN] Sec. 18.6.4, [B] Sec. 4.3, [M] Chap. 8, [HTF] Sec. 4.4
8
 Jan 30
 Perceptrons, single layer neural networks (Lecture slides (Slide 19 revised Feb 20)
 [D] Chapt. 4, [HTF] Chapt. 11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7
9
 Feb 1
 Multi-layer neural networks, backpropagation (Lecture slides)
 [D] Chapt. 10, [HTF] Chapt. 11, [B] Sec. 5.2, 5.3, [M] Sec. 16.5, [RN] Sec. 18.7

Feb 3
 Assignment 2 due (11:59 pm)
10
 Feb 6
 Kernel methods (Lecture slides)
 [D] Chapt. 11, [B] Sec. 6.1, 6.2 [M] Sec. 14.1, 14.2 [H] Chap. 9 [HTF] Chap. 6
11
 Feb 8
 Gaussian Processes (Lecture slides)
 [B] Sec. 6.4 [M] Chap. 15 [HTF] Sec. 8.3
12
 Feb 13
 Support vector machines (Lecture slides)
 [B] Sec. 7.1 [D] Sec. 11.5-11.6 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4
13
 Feb 15
 Midterm (in class)
  • HH1102: Students with last name starting with A-L
  • PAS2083: Students with last name starting with M-Z

Feb 20
 Family day (no class)

Feb 22
 Reading break (no class)
14
 Feb 27
 Support vector machines continued (Lecture slides)

Feb 27
 Project proposal due (11:59 pm)
 [B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4
15
 Mar 1
 Hidden Markov models (Lecture slides (Slide 16 revised April 10) (HMM example (revised March 17))
 [RN] Sec. 15.3 [B] Sec. 13.1-13.2 [M] Sec. 17.3-17.5

Mar 3
 Assignment 3 due (11:59 pm)
16
 Mar 6
 Deep neural networks (Lecture slides) [GBC] Chap. 6, 7, 8
17
 Mar 8
 Convolutional neural networks (Lecture slides (Slide 6 revised March 15))
 [GBC] Chap. 9
18
 Mar 13
 Recurrent and recursive neural networks (Lecture slides)
 [GBC] Chap. 10
19
 Mar 15
 Autoencoders (Lecture slides)
 [GBC] Chap. 14

Mar 17
 Assignment 4 due (11:59 pm)
20
 Mar 20
 Generative networks (variational autoencoders and generative adversarial networks)  (Lecture slides) [GBC] Chap. 20
21
 Mar 22
 Ensemble learning: bagging and boosting (Lecture slides)
 [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 15-16, [D] Chap. 11
22
 Mar 27
 Bagging, decision forests and distributed computing (Lecture slides)
 [RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 15-16, [D] Chap. 11
23
 Mar 29
 Stream learning and course wrap up (Lecture slides)
 [M] Sec. 8.5

Mar 31
 Assignment 5 due (11:59 pm)
24
 Apr 3
 Question answer session

Apr 14
 Project report due (11:59 pm)