CS 6375: Machine Learning

Spring 2022

Introduction to machine learning theory and practice. Learning outcomes:

• Ability to understand and apply basic learning algorithms
• Ability to understand and apply computational learning theories
• Ability to understand and apply advanced learning algorithms

Where: ECSS 2.201 (virtual via Blackboard Collaborate until Feb. 4)
When: TR, 2:30pm-3:45pm

Instructor: Nicholas Ruozzi
Office Hours: Tues. 1:30pm-2:30pm (in person), Wed. 11:00am-12:00pm (online via eLearning), and by appointment in ECSS 3.409.

TA: Yangxiao Lu
Office Hours: Tues. 4:30pm-5:30pm on Teams

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. 18 & 20	Introduction & Regression	Bishop, Ch. 1 Sections 1 & 2 of Andrew Ng's Lecture Notes
2	Jan. 25 & Jan. 27	Perceptron	Bishop, Ch. 7 for a different perspective Barber, Ch. 17.5
3	Feb. 1 & 3	Support Vector Machines	Andrew Ng's SVM Notes
4	Feb. 8 & 10	Duality & Kernel Methods	Boyd, Ch. 5
5	Feb. 15 & 17	Support Vector Machines with Slack Decision Trees	Bishop, Ch. 7.1 Mitchell, Ch. 3 Bishop, Ch. 14.4 Online Probability Notes
6	Feb. 22	k-nearest Neighbor
7	March 1 & 3	Learning Theory & PAC Bounds VC Dimension	Andrew Ng's Lecture Notes
7	March 8 & 10	Bias/Variance Trade-off & Bagging Boosting	Bishop, Ch. 14 Hastie et al., Ch. 8.7 & Ch. 15 Short Intro. to AdaBoost
8	March 22 & 24	Midterm Exam Boosting Continued...
9	March 29 & 31	Clustering PCA	Hastie et al., Ch. 14.3.4, 14.3.6, 14.3.12 Bishop, Ch. 9.1
10	April 5 & 7	More PCA Bayesian Methods	Bishop, 2-2.3.4, 4.2-4.3.4
11	April 12 & 14	Naive Bayes Logistic Regression	Bishop, 4.3.2
12	April 19 & 21	Gaussian Mixture Models	Bishop 8.1, 5.1-5.3
13	April 26 & 28	Neural Networks	Nielsen Ch. 1-3 ConvNetJS Demos
14	May 3 & 5	Reinforcement Learning	Nielsen Ch. 3

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

Exams will be either in-person or take-home and turned in on eLearning (depending on university decisions).
Midterm: March 22, in class
Final: TBD, in person?

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.