CS 6375: Introduction to Machine Learning

Spring 2020

Introduction to machine learning theory and practice. Learning outcomes:

• Ability to understand and apply basic learning algorithms: decision trees, nearest neighbor, SVMs, neural networks, etc.
• Ability to evaluate the performance of learning algorithms on real data.
• Ability to explain the trade-offs, both practical and theoretical, of different learning strategies for classification and regression.

Where: ECSN 2.126
When: TR, 2:30pm-3:45pm

Instructor: Nicholas Ruozzi
Office Hours: T 1:30pm-2:30pm, W 11:00am-12:00pm, and by appointment in ECSS 3.409

TA: Hailiang Dong
Office Hours: M 6:00pm-7:00pm, R 11:00am-12pm in ECSS 2.104A1

Grading: problem sets (50%), midterm (20%), final (30%)

Prerequisites: CS5343 Algorithm Analysis and Data Structures. Familiarity with programming, basic probability, algorithms, multivariable calculus, and linear algebra is also assumed.

Week	Dates	Topic	Readings
1	Jan. 14 & 16	Introduction & Regression Perceptron	Bishop, Ch. 1 Sections 1 & 2 of Andrew Ng's Lecture Notes
2	Jan. 21 & Jan. 23	Support Vector Machines Duality & Kernel Methods	Lecture Notes by Andrew Ng Bishop, Ch. 7 for a different perspective Barber, Ch. 17.5 Boyd, Ch. 5
3	Jan. 28 & Jan. 30	More Duality and Kernel Methods Support Vector Machines with Slack	Bishop, Ch. 7.1, SVMs & SVMs with slack
4	Feb. 4 & Feb. 6	Decision Trees	Mitchell, Ch. 3 Bishop, Ch. 14.4 Online Probability Notes
5	Feb. 11 & 13	k Neareset Neighbor Learning Theory & PAC Bounds	Ng, PAC Learning Notes
6	Feb. 18 & 20	VC Dimension & Bias/Variance Trade-off	Bishop, Ch. 14
7	Feb. 25 & Feb. 27	Bias/Variance Trade-off & Bagging Boosting	Hastie et al., Ch. 8.7 & Ch. 15 Short Intro. to AdaBoost
8	March 3 & 5	Midterm More Boosting
9	March 10 & 12	Clustering PCA	Hastie et al., Ch. 14.3.6, 14.3.8, 14.3.9, 14.3.12 Bishop, Ch. 9.1 PCA Notes
10	March 31 & April 2	More PCA Bayesian Methods	Bishop, 1.5, 4.2-4.3.4 Bishop, 2-2.3.4 Bishop, 8.4.1, 9.2-9.4
11	April 7 & 9	Naive Bayes	Bishop, 8.4.1, 9.2, 9.3, 9.4
12	April 14 & 16	Logistic Regression Gaussian Mixture Models	Bishop 8.1, 5.1-5.3 Other Mixture Model Notes (1) (2)
13	April 21 & 23	Neural Networks	Nielsen Ch. 1-3 ConvNetJS Demos
14	April 28 & 30	Reinforcement Learning	Nielsen Ch. 3 Reinforcement Learning Notes

All problem sets will be available on eLearning and are to be turned in there. See the homework guidelines below for homework policies.

There is no required textbook, but the following books may serve as useful references for different parts of the course.

• Introduction to Machine Learning by Ethem Alpaydin.
• Recognition and Machine Learning by Christopher M. Bishop
• Bayesian Reasoning and Machine Learning by David Barber (free online)
• Machine Learning by Tom Mitchell
• Machine Learning: a Probabilistic Perspective by Kevin Murphy

All exams will be closed book and closed notes.
Midterm: TBD, in class
Final: TBD, during exam period

I expect you to try solving each problem set on your own. However, if you get stuck on a problem, I encourage you to collaborate with other students in the class, subject to the following rules:

1. You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.
2. Once you solve the homework, you must write up your solutions on your own, without looking at other people's write-ups or giving your write-up to others.
3. In your solution for each problem, you must write down the names of any person with whom you discussed it. This will not affect your grade.
4. Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!

Late homework will NOT be accepted except in extreme circumstances or those permitted by university policy (e.g., a religious holiday). All such exceptions MUST be cleared in advance of the due date.

UT Dallas Course Policies and Procedures

For a complete list of UTD policies and procedures, see here.