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CS 188: Introduction to Artificial Intelligence

CS 188: Introduction to Artificial Intelligence (Spring 2014, UC Berkeley). Instructor: Professor Pieter Abbeel. 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.

Introduction


Lecture 01 - Introduction
Lecture 02 - Uninformed Search
Lecture 03 - Informed Search: A* Search and Heuristics
Lecture 04 - Constraint Satisfaction Problems (CSPs)
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 - Markov Models
Lecture 14 - Hidden Markov Models (HMMs)
Lecture 15 - Applications of HMMs, Speech Recognition
Lecture 16 - Bayes' Nets: Representation
Lecture 17 - Bayes' Nets: Independence
Lecture 18 - Bayes' Nets: Inference
Lecture 19
Lecture 20 - Decision Networks and Value of Perfect Information
Lecture 21 - Machine Learning: Naive Bayes
Lecture 22 - Machine Learning: Perceptrons
Lecture 23 - Machine Learning: Kernels and Clustering
Lecture 24 - Advanced Applications: Natural Language Processing, Games, and Robotic Cars
Lecture 25 - Advanced Applications: Computer Vision and Robotics

References
CS 188 - Artificial Intelligence (Spring 2014)
Instructor: Professor Pieter Abbeel. Course Info. Syllabus. Course Policies. Exams (solutions). Lecture Slides. Video. Assignment. Basic ideas and techniques underlying the design of intelligent computer systems.