| Lecture 1 | Introduction | Dan Klein | Fall 2012 |
| Lecture 2 | Uninformed Search | Dan Klein | Fall 2012 |
| Lecture 3 | Informed Search | Dan Klein | Fall 2012 |
| Lecture 4 | Constraint Satisfaction Problems I | Dan Klein | Fall 2012 |
| Lecture 5 | Constraint Satisfaction Problems II | Dan Klein | Fall 2012 |
| Lecture 6 | Adversarial Search | Dan Klein | Fall 2012 |
| Lecture 7 | Expectimax and Utilities | Dan Klein | Fall 2012 |
| Lecture 8 | Markov Decision Processes I | Dan Klein | Fall 2012 |
| Lecture 9 | Markov Decision Processes II | Dan Klein | Fall 2012 |
| Lecture 10 | Reinforcement Learning I | Dan Klein | Fall 2012 |
| Lecture 11 | Reinforcement Learning II | Dan Klein | Fall 2012 |
| Lecture 12 | Probability | Pieter Abbeel | Spring 2014 |
| Lecture 13 | Markov Models | Pieter Abbeel | Spring 2014 |
| Lecture 14 | Hidden Markov Models | Dan Klein | Fall 2013 |
| Lecture 15 | Applications of HMMs / Speech | Pieter Abbeel | Spring 2014 |
| Lecture 16 | Bayes’ Nets: Representation | Pieter Abbeel | Spring 2014 |
| Lecture 17 | Bayes’ Nets: Independence | Pieter Abbeel | Spring 2014 |
| Lecture 18 | Bayes’ Nets: Inference | Pieter Abbeel | Spring 2014 |
| Lecture 19 | Bayes’ Nets: Sampling | Pieter Abbeel | Fall 2013 |
| Lecture 20 | Decision Diagrams / Value of Perfect Information | Pieter Abbeel | Spring 2014 |
| Lecture 21 | Machine Learning: Naive Bayes | Nicholas Hay | Spring 2014 |
| Lecture 22 | Machine Learning: Perceptrons | Pieter Abbeel | Spring 2014 |
| Lecture 23 | Machine Learning: Kernels and Clustering | Pieter Abbeel | Spring 2014 |
| Lecture 24 | Advanced Applications: NLP, Games, and Robotic Cars | Pieter Abbeel | Spring 2014 |
| Lecture 25 | Advanced Applications: Computer Vision and Robotics | Pieter Abbeel | Spring 2014 |
http://ai.berkeley.edu/lecture_videos.html
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