Lecture Videos

The lecture videos from the most recent offerings of CS188 are posted below.

Spring 2014 Lecture Videos
Fall 2013 Lecture Videos
Spring 2013 Lecture Videos
Fall 2012 Lecture Videos
Step-By-Step Supplementary Videos

Spring 2014

Lecture TitleLecturerNotes
Lecture 1 Introduction Pieter Abbeel
Lecture 2 Uninformed Search Pieter Abbeel
Lecture 3 Informed Search Pieter Abbeel
Lecture 4 Constraint Satisfaction Problems I Pieter Abbeel Recording is a bit flaky, see Fall 2013 Lecture 4 for alternative
Lecture 5 Constraint Satisfaction Problems II Pieter Abbeel
Lecture 6 Adversarial Search Pieter Abbeel
Lecture 7 Expectimax and Utilities Pieter Abbeel
Lecture 8 Markov Decision Processes I Pieter Abbeel
Lecture 9 Markov Decision Processes II Pieter Abbeel
Lecture 10 Reinforcement Learning I Pieter Abbeel
Lecture 11 Reinforcement Learning II Pieter Abbeel
Lecture 12 Probability Pieter Abbeel
Lecture 13 Markov Models Pieter Abbeel
Lecture 14 Hidden Markov Models Pieter Abbeel Recording is a bit flaky, see Fall 2013 Lecture 18 for alternative
Lecture 15 Applications of HMMs / Speech Pieter Abbeel
Lecture 16 Bayes' Nets: Representation Pieter Abbeel
Lecture 17 Bayes' Nets: Independence Pieter Abbeel
Lecture 18 Bayes' Nets: Inference Pieter Abbeel
Lecture 19 Bayes' Nets: Sampling Pieter Abbeel Unrecorded, see Fall 2013 Lecture 16
Lecture 20 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 21 Machine Learning: Naive Bayes Nicholas Hay
Lecture 22 Machine Learning: Perceptrons Pieter Abbeel
Lecture 23 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 24 Advanced Applications: NLP, Games, and Robotic Cars Pieter Abbeel
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Pieter Abbeel Unrecorded


Fall 2013

Lecture TitleLecturerNotes
Lecture 1 Introduction Dan Klein
Lecture 2 Uninformed Search Dan Klein
Lecture 3 Informed Search Dan Klein
Lecture 4 Constraint Satisfaction Problems I Dan Klein
Lecture 5 Constraint Satisfaction Problems II Dan Klein
Lecture 6 Adversarial Search Dan Klein
Lecture 7 Expectimax and Utilities Dan Klein
Lecture 8 Markov Decision Processes I Dan Klein
Lecture 9 Markov Decision Processes II Dan Klein
Lecture 10 Reinforcement Learning I Dan Klein
Lecture 11 Reinforcement Learning II Dan Klein
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Dan Klein
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Dan Klein
Lecture 19 Applications of HMMs / Speech Dan Klein
Lecture 20 Machine Learning: Naive Bayes Dan Klein
Lecture 21 Machine Learning: Perceptrons Dan Klein
Lecture 22 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 23 Machine Learning: Decision Trees and Neural Nets Pieter Abbeel
Lecture 24 Advanced Applications: NLP and Robotic Cars Dan Klein Unrecorded, see Spring 2013 Lecture 24
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Dan Klein,
Pieter Abbeel		 Unrecorded

Spring 2013

Lecture TitleLecturerNotes
Lecture 1 Introduction Pieter Abbeel Video Down
Lecture 2 Uninformed Search Pieter Abbeel
Lecture 3 Informed Search Pieter Abbeel
Lecture 4 Constraint Satisfaction Problems I Pieter Abbeel
Lecture 5 Constraint Satisfaction Problems II Pieter Abbeel Unrecorded, see Fall 2012 Lecture 5
Lecture 6 Adversarial Search Pieter Abbeel
Lecture 7 Expectimax and Utilities Pieter Abbeel
Lecture 8 Markov Decision Processes I Pieter Abbeel
Lecture 9 Markov Decision Processes II Pieter Abbeel
Lecture 10 Reinforcement Learning I Pieter Abbeel
Lecture 11 Reinforcement Learning II Pieter Abbeel
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Pieter Abbeel
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Pieter Abbeel
Lecture 19 Applications of HMMs / Speech Pieter Abbeel
Lecture 20 Machine Learning: Naive Bayes Pieter Abbeel
Lecture 21 Machine Learning: Perceptrons I Nicholas Hay
Lecture 22 Machine Learning: Perceptrons II Pieter Abbeel
Lecture 23 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 24 Advanced Applications: NLP and Robotic Cars Pieter Abbeel
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Pieter Abbeel Unrecorded

Fall 2012

Lecture TitleLecturerNotes
Lecture 1 Introduction Dan Klein
Lecture 2 Uninformed Search Dan Klein
Lecture 3 Informed Search Dan Klein
Lecture 4 Constraint Satisfaction Problems I Dan Klein
Lecture 5 Constraint Satisfaction Problems II Dan Klein
Lecture 6 Adversarial Search Dan Klein
Lecture 7 Expectimax and Utilities Dan Klein
Lecture 8 Markov Decision Processes I Dan Klein
Lecture 9 Markov Decision Processes II Dan Klein
Lecture 10 Reinforcement Learning I Dan Klein
Lecture 11 Reinforcement Learning II Dan Klein
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Pieter Abbeel
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Pieter Abbeel
Lecture 19 Applications of HMMs / Speech Dan Klein
Lecture 20 Machine Learning: Naive Bayes Dan Klein
Lecture 21 Machine Learning: Perceptrons Dan Klein
Lecture 22 Machine Learning: Kernels and Clustering Dan Klein
Lecture 23 Machine Learning: Decision Trees and Neural Nets Pieter Abbeel
Lecture 24 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 25 Advanced Applications: NLP and Robotic Cars Dan Klein,
Pieter Abbeel		 Unrecorded
Lecture 26 Conclusion Dan Klein,
Pieter Abbeel		 Unrecorded

Step-By-Step Supplementary Videos

Lecture TitleLecturerNotes
SBS-1 DFS and BFS Pieter Abbeel Lec: Uninformed Search
SBS-2 A* Search Pieter Abbeel Lec: Informed Search
SBS-3 Alpha-Beta Pruning Pieter Abbeel Lec: Game playing
SBS-4 D-Separation Pieter Abbeel Lec: Bayes' nets: Syntax and semantics
SBS-5 Elimination of One Variable Pieter Abbeel Lec: Bayes' nets: Exact inference
SBS-6 Variable Elimination Pieter Abbeel Lec: Bayes' Nets: Exact inference
SBS-7 Sampling Pieter Abbeel Lec: Bayes' nets: Approximate inference
SBS-8 Gibbs' Sampling Michael Liang Lec: Bayes' nets: Approximate inference
SBS-9 Perceptrons Pieter Abbeel Lec: Machine Learning: Neural networks
SBS-10 Maximum Likelihood Pieter Abbeel Lec: Machine Learning: Statistical learning
SBS-11 Laplace Smoothing Pieter Abbeel Lec: Machine Learning: Statistical learning