UC Berkeley CS188 Intro to AI – Course Materials

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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