CS 189: Introduction to Machine Learning
Taught Spring 2016, Fall 2016, Spring 2017
- Section 1: Basics and math review [PDF]
- Section 2: Gradient Descent, SVM [PDF]
- Section 3: Decision Theory, Generative Models, Gaussian Classifiers [PDF]
- Section 4: Covariance Matrices [PDF]
- Section 5: Regression [PDF]
- Section 6: Regression, Newton's Method, Risk Functions [PDF]
- Section 7: Bias-variance, Regularization [PDF]
- Section 8: Kernels, Decision Trees [PDF]
- Section 9: Decision Trees, Random Forests [PDF]
- Section 10: Neural Networks [PDF]
- Section 11: Tricks, ConvNets, PCA [PDF]
- Section 12: Unsupervised Learning [PDF]
- Section 13: Recap and advice [PDF]