CS 6375: Machine LearningFall 2018
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||Aug. 20 & 22||Introduction & RegressionPerceptron||Bishop, Ch. 1|
|2||Aug. 27 & Aug. 29||Support Vector MachinesDuality & Kernel Methods||Lecture Notes by Andrew NgBishop, Ch. 7 for a different perspectiveBarber, Ch. 17.5Boyd, 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 Boosting||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 PCA||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|
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.
- Pattern 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 closed book and closed notes. Midterm: Wednesday, October 10, in class Final: Wednesday, December 12, 11:00AM - 1:45PM
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