CS 6375: Machine Learning

Fall 2018

Course Info

Introduction to machine learning theory and practice. Learning outcomes: Where: ECSS 2.305
When: MW, 11:30am-12:45pm

Instructor: Nicholas Ruozzi
Office Hours: Monday 1:00pm-2:00pm, Wed. 10am-11am, and by appointment in ECSS 3.409

TA: Changbin Li
Office Hours: Tuesday 4:00pm-6:00pm in ECSS 2.104A1

Grading: problem sets (50%), midterm (20%), final (30%)
Attendance is MANDATORY. The instructor reserves the right to lower final grades as a result of poor attendance.

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

Schedule & Lecture Slides

Week Dates Topic Readings
1 Aug. 20 & 22 Introduction & Regression
Bishop, Ch. 1
2 Aug. 27 & Aug. 29 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 Sept. 5 Support Vector Machines with Slack Bishop, Ch. 7.1, SVMs & SVMs with slack
4 Sept. 10 & 12 Decision Trees Mitchell, Ch. 3
Bishop, Ch. 14.4
5 Sept. 17 & 19 k Neareset Neighbor
Learning Theory & PAC Bounds
Ng, PAC Learning Notes
6 Sept. 24 & 26 VC Dimension & Bias/Variance Trade-off Bishop, Ch. 14
7 Oct. 1 & 3 Bias/Variance Trade-off & Bagging
Hastie et al., Ch. 8.7 & Ch. 15
Short Intro. to AdaBoost
8 Oct. 8 & 10 Midterm (in class Oct. 10)
More Boosting
9 Oct. 15 & 17 Clustering
Hastie et al., Ch. 14.3.6, 14.3.8, 14.3.9, 14.3.12
Bishop, Ch. 9.1
PCA Notes
10 Oct. 22 & 24 More PCA Bishop, 1.5, 4.2-4.3.4
Bishop, 2-2.3.4
PCA Example
11 Oct. 29 & 31 Bayesian Methods Naive Bayes
Bishop, 8.4.1, 9.2, 9.3, 9.4
12 Nov. 5 & Nov. 7 Logistic Regression
13 Nov. 12 & 14 Gaussian Mixture Models
Neural Networks
Nielsen Ch. 1-3
Bishop 8.1, 5.1-5.3
ConvNetJS Demos
Other Mixture Model Notes (1) (2)
14 Nov. 26 & 28 More Neural Networks
Reinforcement Learning
Nielsen Ch. 3
Reinforcement Learning Notes
14 Dec. 4 & Dec. 6 More Reinforcement Learning Active Learning

Problem Sets

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

Textbooks & References

The following textbook will be used for the course. The following books may also serve as useful references for different parts of the course.


All exams will be closed book and closed notes.
Midterm: Wednesday, October 10, in class
Final: Wednesday, December 12, 11:00AM - 1:45PM

Homework Guidelines*

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 homeworks 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.

*adpated from
David Sontag