I have been a teaching assistant for CS 188 (Introduction to Artificial Intelligence) and CS 189 (Introduction to Machine Learning). Here are the slides I've made for discussion sections.

- Section 1: Search [PDF]
- Section 5: MDPs, RL [PDF]
- Section 6: Random Variables, Conditional Probability, Bayes' Rule [PDF]
- Section 7: Bayes' Nets: Representation, Independence [PDF]
- Section 8: Bayes' Nets: Sampling [PDF]
- Section 9: VPI [PDF]
- Section 10: Markov Models [PDF]
- Section 11: Naive Bayes [PDF]
- Section 12: Perceptrons [PDF]
- Section 13: Feature Maps, K-means [PDF]

- Section 1: Probability, Linear Algebra, Matrix Derivatives [PDF]
- Section 2: Gradient Descent, Perceptron, SVM [PDF]
- Section 3: Gaussian Classifiers [PDF]
- Section 4: Covariance Matrices [PDF]
- Section 5: Linear and Logistic Regression [PDF]
- Section 6: Risk Functions, Bias-Variance, Regularization [PDF]
- Section 7: Mean and variance of sample mean, Estimators [PDF]
- Section 8: Neural Networks [PDF]
- Section 9: Performance tips, Convolutional Neural Networks [PDF]
- Section 10: Kernels, Decision Trees [PDF]
- Section 11: Unsupervised Learning [PDF]
- Section 12: Advice and other cool things [PDF]