CS 6375: Introduction to Machine Learning

Spring 2020

Course Info

Introduction to machine learning theory and practice. Learning outcomes: 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.

Schedule & Lecture Slides

Week Dates Topic Readings
1 Jan. 14 & 16 Introduction & Regression
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
Hastie et al., Ch. 8.7 & Ch. 15
Short Intro. to AdaBoost
8 March 3 & 5 Midterm
More Boosting
9 March 10 & 12 Clustering
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

Problem Sets

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

Textbooks & References

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


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

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 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.

*adpated from
David Sontag