CS 6375: Machine Learning

Spring 2021

Course Info

Introduction to machine learning theory and practice. Learning outcomes: Where: Blackboard Collaborate
When: TR, 2:30pm-3:45pm

Instructor: Nicholas Ruozzi
Office Hours: T 3:45pm-4:45pm, W 11:00am-12:00pm, and by appointment on Blackboard

TA: TBD
Office Hours: TBD

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. 19 & 21 Introduction & Regression Bishop, Ch. 1
Sections 1 & 2 of Andrew Ng's Lecture Notes
2 Jan. 26 & Jan. 28 Perceptron Bishop, Ch. 7 for a different perspective
Barber, Ch. 17.5
3 Feb. 2 & 4 Support Vector Machines
Duality & Kernel Methods
Boyd, Ch. 5
4 Feb. 9 More Duality and Kernel Methods
5 Feb. 23 & 25 Support Vector Machines with Slack
Decision Trees
Bishop, Ch. 7.1
Mitchell, Ch. 3
Bishop, Ch. 14.4
Online Probability Notes
6 March 2 & 4 More Decision Trees
k Neareset Neighbor

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.

Exams

All exams will be take-home and turned in on eLearning.
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