CS 188: Artificial Intelligence
CS 188: Artificial Intelligence (Spring 2012, 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.
| Lecture 01 - Introduction |
| Lecture 03 - A* Search and Heuristics |
| Lecture 04 - A* Search and Heuristics (cont.), Constraint Satisfaction Problems |
| Lecture 06 - Search for Games |
| Lecture 07 - Search for Games (cont.) |
| Lecture 08 - Utility Theory, Markov Decision Processes |
| Lecture 09 - Markov Decision Processes (cont.) |
| Lecture 10 - Reinforcement Learning |
| Lecture 11 - Midterm Preparation Lecture |
| Lecture 12 - Reinforcement Learning (cont.) |
| Lecture 13 - Probability |
| Lecture 14 - Probability; Independence, Bayes' Nets |
| Lecture 15 - Bayes' Nets: Representation and Independence |
| Lecture 17 - Bayes' Nets: Sampling |
| Lecture 18 - Midterm Review Lecture |
| Lecture 20 - Hidden Markov Models (HMMs) and Particle Filtering |
| Lecture 21 - HMMs and Particle Filtering (cont.), Speech Recognition |
| Lecture 22 - Decision Diagrams |
| Lecture 24 - Perceptrons |
| Lecture 25 - Perceptrons (cont.) |
| Lecture 26 - Perceptrons (cont.), Advanced Applications: Robotics |
| Lecture 27 - Advanced Applications: Robotics, Computer Vision, Language |
| Lecture 28 - Review: Search, CSPs, Game Trees |
| Lecture 29 - Review: Probability, Bayes' Nets |