Lecture 1: Introduction
Lecture 2: Linear Regression I
Assignment 1 Out
Lecture 3: Linear Regression II
Lecture 4: Linear Regression III
Lecture 5: Features and Basis Functions
Assignment 1 Due at 23:55
Lecture 6: Overfitting and Regularization
Assignment 2 Out
Lecture 7: Cross Validation
Lecture 8: Linear Classification I
Lecture 9: Linear Classification II
Lecture 10: Linear Classification III -- Support Vector Machines
Assignment 2 Due at 23:55
Assignment 3 Out
Lecture 11: Kernel-based Classification
MIDTERM EXAM (Covers through Lecture 10 on 6 March)
Lecture 12: Neural Networks I
Lecture 13: Neural Networks II
Assignment 3 Due at 23:55
Lecture 14: K-Means Clustering Assignment 4 Out
Lecture 15: Hierarchical Clustering
Lecture 16: Principal Component Analysis
Lecture 17: SVD and Latent Factor Models
Assignment 4 Due at 23:55
Lecture 18: Markov Decision Processes Assignment 5 Out
Lecture 19: Value Iteration
Lecture 20: Policy Iteration
Lecture 21: Reinforcement Learning I
Assignment 5 Due at 23:55
Lecture 22: Reinforcement Learning II Assignment 6 Out
Lecture 23: Wrap-up
Assignment 6 Due at 23:55