Lecture 1: Introduction to Inference and Learning
Lecture 2: The Gaussian Distribution and Linear Regression [ | cs281_submit.m | wine-train.csv | wine-test.csv ]
Lecture 3: Linear Models for Classification
Lecture 4: K-Means and Mixture Models [ quantize.m | interesting man ]
Lecture 5: Bayes' Rule and Optimal Decisions [ flips.m | gaussest.m ]
Lecture 6: Exponential Family Models [ flips_beta.m ]
Lecture 7: Graphical Models
Lecture 8: Belief Propagation
Lecture 9: Monte Carlo Basics
Lecture 10: Advanced Markov Chain Monte Carlo
Lecture 11: Variational Inference and Deterministic Approximation
Lecture 12: Principal Components Analysis and Friends
Lecture 13: Topic Models and Admixtures
Lecture 14: Probabilistic Matrix Factorization for Relational Data
Midterm: Covers Lectures 1-13
Lecture 15: Models for Time Series and Sequential Data
Lecture 16: Inference for Time Series and Sequential Data
Lecture 17: Gaussian Process I
Lecture 18: Gaussian Process II
Lecture 19: Dirichlet Process I
Lecture 20: Dirichlet Process II
Lecture 21: Indian Buffet Process
Lecture 22: Nonlinear Dimensionality Reduction
Lecture 23: Support Vector Machines and Kernel Methods
Lecture 24: Undirected Graphical Models
Lecture 25: Deep Belief Networks
Special: Project Presentation
Projects Due
