CS489/698 Winter 2018 - Introduction to Machine Learning

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 3	Introduction to Machine Learning (Lecture slides)
2	Jan 8	K-nearest neighbours (Lecture slides (Slide 24 revised on Jan 11))	[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 10	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 15	Statistical learning (Lecture slides)	[RN] Sec. 20.1, 20.2, [M] Sec. 2.2, 3.2
5	Jan 17	Linear regression by maximum likelihood, maximum a posteriori, Bayesian learning (Lecture slides (Slide 11 revised Jan 24, Slide 14 revised Feb 26))	[B] Sec. 3.1-3.3, [M] Chap. 7
	Jan 19	Assignment 1 due (11:59 pm)
6	Jan 22	Mixture of Gaussians (Lecture slides (Slide 16 revised Jan 24, Slide 11 revised Feb 2))	[B] Sec. 4.2, [M] Sec. 4.2
7	Jan 24	Logistic regression, generalized linear models (Lecture slides (Slide 11 revised Feb 12, Slide 5 revised Jan 30))	[RN] Sec. 18.6.4, [B] Sec. 4.3, [M] Chap. 8, [HTF] Sec. 4.4
8	Jan 29	Perceptrons, single layer neural networks (Lecture slides (Slide 24 revised Feb 12))	[D] Chapt. 4, [HTF] Chapt. 11, [B] Sec. 4.1.7, 5.1, [M] Sec. 8.5.4, [RN] Sec. 18.7
9	Jan 31	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 2	Assignment 2 due (11:59 pm)
10	Feb 5	Guest lecture by Joseph D'Souza (ProNavigator), Nabiha Asghar (UW and ProNavigator) (ProNavigator lecture slides), Francois Chaubard (Focal Systems) and Agastya Kalra (Focal Systems)
11	Feb 7	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
12	Feb 12	Gaussian Processes (Lecture slides)	[B] Sec. 6.4 [M] Chap. 15 [HTF] Sec. 8.3
13	Feb 14	Midterm (in class) RCH307: Students with last name starting with A-F RCH309: Students with last name starting with G-M MC1085: Students with last name starting with N-Z
	Feb 19	Family day (no class)
	Feb 21	Reading break (no class)
14	Feb 26	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
	Feb 26	Project proposal due (11:59 pm)
15	Feb 28	Support vector machines continued (Lecture slides)	[B] Sec. 7.1 [D] Sec. 6.7 [HTF] Chap. 12 [M] Sec. 14.5 [RN] Sec. 18.9 [MRT] Chap. 4
	Mar 2	Assignment 3 due (11:59 pm)
16	Mar 5	Deep neural networks (Lecture slides)	[GBC] Chap. 6, 7, 8
17	Mar 7	Convolutional neural networks (Lecture slides)	[GBC] Chap. 9
18	Mar 12	Hidden Markov models (Lecture slides)	[RN] Sec. 15.3 [B] Sec. 13.1-13.2 [M] Sec. 17.3-17.5
19	Mar 14	Recurrent and recursive neural networks (Lecture slides (Slide 10 revised March 21))	[GBC] Chap. 10
	Mar 16	Assignment 4 due (11:59 pm)
20	Mar 19	Autoencoders (Lecture slides (Slide 9 revised March 21))	[GBC] Chap. 14
21	Mar 21	Generative networks (variational autoencoders and generative adversarial networks) (Lecture slides)	[GBC] Chap. 20
22	Mar 26	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
23	Mar 28	Bagging, decision forests, distributed computing (Lecture slides)	[RN] Sec 18.10, [M] Sec. 16.2.5, [B] Chap. 14, [HTF] Chap. 15-16, [D] Chap. 11, [M] Sec. 8.5
	Mar 30	Assignment 5 due (11:59 pm)
24	Apr 2	Guest Lecture by Focal Systems: Machine Learning in the Real World
	Apr 10	Final exam 12:30-3pm M3-1006
	Apr 15	Project report due (11:59 pm)