CS 6375: Introduction to Machine LearningSpring 2020
Introduction to machine learning theory and practice. Learning outcomes:
- Ability to understand and apply basic learning algorithms: decision trees, nearest neighbor, SVMs, neural networks, etc.
- Ability to evaluate the performance of learning algorithms on real data.
- Ability to explain the trade-offs, both practical and theoretical, of different learning strategies for classification and regression.
Schedule & Lecture Slides
|1||Jan. 14 & 16||Introduction & RegressionPerceptron||Bishop, Ch. 1Sections 1 & 2 of Andrew Ng's Lecture Notes|
|2||Jan. 21 & Jan. 23||Support Vector MachinesDuality & Kernel Methods||Lecture Notes by Andrew NgBishop, Ch. 7 for a different perspectiveBarber, Ch. 17.5Boyd, 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 Boosting||Hastie et al., Ch. 8.7 & Ch. 15 Short Intro. to AdaBoost|
|8||March 3 & 5||Midterm More Boosting|
|9||March 10 & 12||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||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|
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 closed book and closed notes. 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