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Overview • Schedule • References • Office Hours • Team Project

Note: The materials on this webpage are available on Canvas.

Overview

Machine learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as clustering, decision trees, neural network learning, statistical learning methods, Bayesian learning methods, dimension reduction, kernel methods, and reinforcement learning. Programming assignments include implementation and hands-on experiments with various learning algorithms.

Schedule

Note: This schedule is subject to change.

Week	Date	Topic	Readings	Announcements
1	Aug 19	Introduction |
	Aug 21	Defining ML |	[HD] Sec 1.1 + 1.2
			Wagstaff. (2011)
2	Aug 24	Decision Trees I |	[HD] Chapter 1
	Aug 26	Decision Trees II |
	Aug 28	Limits and Eval I |	[HD] Sec 2.1 - 2.4	HW 1: Released
			Domingos. (2012)
3	Aug 31	Limits and Eval II |
	Sept 2	IB Learning I |	[HD] Ch.3
	Sept 4	IB Learning II |		HW 1: Due
4	Sept 7	[ No Lecture ]		Project 1: Released
	Sept 9	Perceptron I |	[HD] Ch.4
	Sept 11	Perceptron II |
5	Sept 14	Linear Models I |	[HD] Ch.7
	Sept 16	[ No Lecture ]
	Sept 18	Project 1: Q&A
6	Sept 21	Linear Models II |	[HD] Ch.7	Project 1: Due
	Sept 23	Prob. Methods I |	[TM] Ch.2	HW 2: Released
	Sept 25	Team Meeting		TP: M1 Released
7	Sept 28	Prob. Methods II |
	Sept 30	Linear Classification I |	[TM] Ch.3	HW 2: Due
	Oct 2	Building Datasets		Project 2: Released
8	Oct 5	Linear Classification II |
	Oct 7	[ No Lecture ]
	Oct 9	Team Meeting		TP: M1 Due
				TP: All Released
9	Oct 12	SVMs |	[HD] Ch.7.7
	Oct 14	Kernel Methods |	[HD] Ch.11
	Oct 16	Team Meeting		on Canvas.
10	Oct 19	US Learning I |	[HD] Ch.15	Project 2: Due
	Oct 21	US Learning II |
	Oct 23	Team Meeting		HW 3: Released
				Project 3: Released
11	Oct 26	Neural Nets I |	[IG] Ch.6 & 9
	Oct 28	Neural Nets II |	[IG] Ch.10 & 15
	Oct 30	Team Meeting		TP: M2 Due
12	Nov 2	Ensemble Methods |	[HD] Ch.13
	Nov 4	Active Learning |	[BS] Ch.1-3
	Nov 6	Team Meeting		HW3: Due
13	Nov 9	Interpretable ML |	[CM] All	HW4: Released
	Nov 11	Reinfor. Learning |	[RS] Ch.1-6	Project 3: Due
				Project 4: Released
	Nov 13	Team Meeting
14	Nov 16	[ Team Project Time ]
	Nov 18	[ Team Project Time ]
	Nov 20	Team Meeting		TP: M3 Due
15	Nov 23	The Future of ML
	Nov 25			HW4: Due
	Nov 27
	Dec 4	Project Presentations		TP: M4 Due
				Project 4: Due

References

This course will make periodic reference to the following online references

Textbook References

• [HD] H. Daume III. A Course in Machine Learning. (In Progress)
• [TM] T. Mitchell. Machine Learning. 1997.
• [BS] B. Settles. Active Learning. 2012.
• [RS] R. Sutton and A. Barto. Reinforcement Learning: An Introduction. 2018.
• [IG] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. 2016.
• [XZ] X. Zhu, and A. Goldberg. Introduction to Semi-Supervised Learning. 2009.
• [CM] C. Molnar. Interpretable Machine Learning. 2020.
• [SB] S. Barocas, M. Hardt, and A. Narayanan. Fairness and Machine Learning. 2020.