CS 181
General
Syllabus
Calendar
Staff
Office Hours
Resources
Beyond CS 181
Schedule
Lecture Recaps
Lecture 1 (Welcome to CS181)
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 12 (Support Vector Machines II)
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)
Lecture 23 (Final Lecture)
Sections
HW
The Cube
Supervised Learning
Discrete
Continuous
Probabilistic
Lecture 5 - Probabilistic Classification
Lecture 3 - Probabilistic Regression
Nonprobabilistic
Lecture 1 - KNN Classification
Lecture 4 - Linear Classification
Lecture 8 - Neural Networks 1
Lecture 9 - Neural Networks 2
Lecture 10 - Support Vector Machines 1
Lecture 11 - Support Vector Machines 2
Lecture 1 - KNN and Kernel Regression
Lecture 2 - Linear Regression
Lecture 8 - Neural Networks 1
Lecture 9 - Neural Networks 2
Unsupervised Learning
Discrete
Continuous
Probabilistic
Lecture 14 - Mixture Models
Lecture 16 - Topic Models
Nonprobabilistic
Lecture 13 - HAC and K-Means
Lecture 15 - Principal Component Analysis