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CS 188: Introduction to Artificial Intelligence

CS 188: Introduction to Artificial Intelligence (Spring 2015, UC Berkeley). Instructors: Professor Pieter Abbeel and Professor Dan Klein. This course introduces the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. Topics include heuristic search, problem solving, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, expert systems, learning, perception, language understanding.

Lecture 04 - Graph Search, Constraint Satisfaction Problems

Time	Lecture Chapters
[00:00:00]	1. Graph Search
[00:25:25]	2. Constraint Satisfaction Problems

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Lecture 01 - Introduction

Lecture 02 - Agents and Search: Uninformed Search

Lecture 03 - Informed Search: A* Search and Heuristics

Lecture 04 - Graph Search, Constraint Satisfaction Problems

Lecture 05 - Constraint Satisfaction Problems (cont.)

Lecture 06 - Adversarial Search: Game Trees, Minimax

Lecture 07 - Game Trees: Expectimax Search; Utilities

Lecture 08 - Markov Decision Processes

Lecture 09 - Markov Decision Processes (cont.)

Lecture 10 - Reinforcement Learning

Lecture 11 - Reinforcement Learning (cont.)

Lecture 12 - Probability

Lecture 13 - Bayes' Nets I: Representation

Lecture 14 - Bayes' Nets II: Independence

Lecture 15 - Bayes' Nets III: Inference

Lecture 16 - Bayes' Nets IV: Sampling

Lecture 17 - Decision Networks and Value of Perfect Information

Lecture 18 - Hidden Markov Models (HMMs)

Lecture 19 - Particle Filtering and Applications of HMMs

Lecture 20 - Machine Learning: Naive Bayes

Lecture 21 - Machine Learning: Perceptrons

Lecture 22 - Machine Learning: Kernels and Clustering

Lecture 23 - Machine Learning: Decision Trees and Neural Nets

Lecture 24 - Natural Language Processing, Games, and Robotic Cars

Lecture 25 - Games, Computer Vision, and Robotics

Lecture 26 - Conclusion