CS 181
General
Syllabus
Calendar
Staff
Office Hours
Resources
Schedule
Lecture Recaps
Lecture 1 (Nonparametric Regression)
Lecture 2 (Linear Regression)
Lecture 3 (Probabilistic Linear Regression)
Lecture 4 (Linear Classification)
Lecture 5 (Probabilistic Classification)
Lecture 6 (Model Specification)
Lecture 7 (Bayesian Model Specification)
Lecture 8 (Neural Networks I)
Lecture 9 (Neural Networks II)
Lecture 10 (Support Vector Machines I)
Lecture 11 (Support Vector Machines II)
Lecture 12 (EthiCS)
Lecture 13 (Clustering)
Lecture 14 (Mixture Models)
Lecture 15 (PCA)
Lecture 16 (Topic Models)
Lecture 17 (Graphical Models)
Lecture 18 (Inference in Bayes Nets)
Lecture 19 (Hidden Markov Models)
Lecture 20 (Markov Decision Processes)
Lecture 21 (Reinforcement Learning I)
Lecture 22 (Reinforcement Learning II)
Sections
HW
Resources
Course Textbook
A version 0.0 of course notes can be found
here
.
Relevant Textbooks
Bishop 2006, Pattern Recognition and Machine Learning
Murphy 2012, Machine Learning: A Probabilistic Perspective
Petersen and Pedersen 2012, The Matrix Cookbook
Murphy 1998, Bayesian Network Tutorial
Rabiner 1989, Tutorial on HMMs
Sutton and Barto 2018, Reinforcement Learning: An Introduction
Deisenroth, Faisal, and Ong 2020, Mathematics for Machine Learning