CS 6375: Machine LearningSpring 2021
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
Schedule & Lecture Slides
|1||Jan. 19 & 21||Introduction & Regression||Bishop, Ch. 1Sections 1 & 2 of Andrew Ng's Lecture Notes|
|2||Jan. 26 & Jan. 28||Perceptron||Bishop, Ch. 7 for a different perspectiveBarber, 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|
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.
- 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 take-home and turned in on eLearning. 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:
- 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.
- 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.
- 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.
- Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!
UT Dallas Course Policies and Procedures
*adpated from David Sontag