Lecture 1: Introduction [slides]
Lecture 2: Linear Regression I
Assignment 1 Out
Lecture 3: Linear Regression II
Lecture 4: Features and Basis Functions
Lecture 5: Overfitting and Regularization
Assignment 1 Due at 23:55
Lecture 6: Cross Validation
Assignment 2 Out
Lecture 7: Linear Classification I
Lecture 8: Linear Classification II
Lecture 9: Linear Classification III -- Support Vector Machines
Assignment 2 Due at 23:55
Lecture 10: Kernel-based Classification
Lecture 11: Neural Networks I
Assignment 3 Out
Lecture 12: Neural Networks II
Midterm Exam
Assignment 3 Due at 23:55
No Class
Assignment 4 Out
Lecture 13: K-Means Clustering
Lecture 14: Hierarchical Clustering
Lecture 15: Principal Component Analysis
Assignment 4 Due at 23:55
Lecture 16: Latent Factor Models
Assignment 5 Out
Lecture 17: Markov Decision Processes
Lecture 18: Value Iteration
Assignment 5 Due at 23:55
Lecture 19: Policy Iteration
Assignment 6 Out
Lecture 20: Model-based Reinforcement Learning
Lecture 21: Model-free Reinforcement Learning