There is no textbook for this class, and we do not plan on posting lecture slides. Instead, see Shewchuk's lecture notes and "A Comprehensive Guide to Machine Learning" on our Resources page, which cover a large portion of the material covered in lecture. We will follow Shewchuk's lecture notes closely. Notes are not a substitute for going to lecture, as additional material may be covered in lecture.
The discussion sections may cover new material and will give you additional practice solving problems. See Syllabus for more information.
- Discussion 1: Support Vector Machines (solution)
- Discussion 2: Review (solution)
- Discussion 3: MLE, MAP, Bayesian Decision Theory (solution)
- Discussion 4: Gaussian Discriminant Analysis, MLE (solution)
- Discussion 5: Gaussian Isocontours, LDA (solution)
- Discussion 6: Regression, Pseudoinverse, SGD (solution)
- Discussion 7: Bias and Variance (solution)