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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 27 - Advanced Applications: Robotics, Computer Vision, Language

Time Lecture Chapters
[00:00:00] 1. Advanced Applications: Robotics (cont.)
[00:36:50] 2. Computer Vision
[00:51:40] 3. Natural Language Processing

Go to the Course Home or watch other lectures:

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