- Recommended Lecture Videos
- We recommend watching the following set of lecture videos:
Lecture Title | Lecturer | Semester | |
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 |
Additionally, there are additional Step-By-Step videos which supplement the lecture's materials. These videos are listed below:
Lecture Title | Lecturer | Notes | |
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: Adversarial Search |
SBS-4 | D-Separation | Pieter Abbeel | Lec: Bayes' Nets: Independence |
SBS-5 | Elimination of One Variable | Pieter Abbeel | Lec: Bayes' Nets: Inference |
SBS-6 | Variable Elimination | Pieter Abbeel | Lec: Bayes' Nets: Inference |
SBS-7 | Sampling | Pieter Abbeel | Lec: Bayes' Nets: Sampling |
SBS-8 | Maximum Likelihood | Pieter Abbeel | Lec: Machine Learning: Naive Bayes |
SBS-9 | Laplace Smoothing | Pieter Abbeel | Lec: Machine Learning: Naive Bayes |
SBS-10 | Perceptrons | Pieter Abbeel | Lec: Machine Learning: Perceptrons |
- Per-Semester Video Archive
- 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
- Spring 2014
Lecture Title | Lecturer | Notes | |
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
Spring 2013
Lecture Title | Lecturer | Notes | |
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
来源: http://ai.berkeley.edu/lecture_videos.html