CS 6375: Machine LearningFall 2017
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. 21 & 23||Introduction & RegressionPerceptron||Bishop, Ch. 1|
|2||Aug. 28 & Aug. 30||Support Vector MachinesDuality & Kernel Methods||Lecture Notes by Andrew NgBishop, Ch. 7 for a different perspectiveBarber, Ch. 17.5Boyd, Ch. 5|
|3||Sept. 6||Support Vector Machines with Slack||Bishop, Ch. 7.1, SVMs & SVMs with slack|
|4||Sept. 11 & 13||Decision Treesk Neareset Neighbor||Mitchell, Ch. 3 Bishop, Ch. 14.4|
|5||Sept. 18 & 20||Learning Theory & PAC Bounds VC Dimension & Bias/Variance Trade-off||Ng, PAC Learning Notes|
|6||Sept. 25 & 27||Bias/Variance Trade-off & Bagging Boosting||Bishop, Ch. 14 Hastie et al., Ch. 8.7 & Ch. 15 Short Intro. to AdaBoost|
|7||Oct. 2 & 4||Clustering PCA||Hastie et al., Ch. 14.3.6, 14.3.8, 14.3.9, 14.3.12 Bishop, Ch. 9.1 PCA Notes|
|8||Oct. 9 & 11||Midterm (in class Oct. 11)||More PCA|
|9||Oct. 16 & 18||Bayesian Methods Naive Bayes|| Bishop, 1.5, 4.2-4.3.4 |
|10||Oct. 23 & 25||Logistic Regression Gaussian Mixture Models||Bishop, 8.4.1, 9.2, 9.3, 9.4 Other Mixture Model Notes (1) (2)|
|11||Oct. 30 & Nov. 1||Hidden Markov Models||Bishop 13.1-2,Other HMM Notes|
|12||Nov. 6 & 8||Bayesian Networks Neural Networks||Intro. to Bayesian Networks Nielsen Ch. 1-3 Bishop 8.1, 5.1-5.3 ConvNetJS Demos|
|12||Nov. 13 & 15||More Neural Networks Reinforcement Learning||Nielsen Ch. 3 Reinforcement Learning Notes|
|13||Nov. 27 & 29||More Reinforcement Learning Collaborative Filtering|
|14||Dec. 4 & Dec. 6||Active Learning Review|
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: Oct. 11, in class Final: Dec. 13, 11am - 1:45pm, ECSS 2.306
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