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CS 188: Artificial Intelligence (Fall 2011, UC Berkeley). Instructor: Professor Dan Klein. This course introduces
FREE
This course includes
Hours of videos
638 years, 9 months
Units & Quizzes
23
Unlimited Lifetime access
Access on mobile app
Certificate of Completion
he 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.
Course Currilcum
- Lecture 01 – Introduction Unlimited
- Lecture 02 – Queue-Based Search Unlimited
- Lecture 03 – A* Search and Heuristics Unlimited
- Lecture 04 – Constraint Satisfaction Problems Unlimited
- Lecture 05 – Constraint Satisfaction Problems (cont.) Unlimited
- Lecture 06 – Adversarial Search: Game Trees, Minimax Unlimited
- Lecture 07 – Expectimax Search Unlimited
- Lecture 08 – Utilities, Markov Decision Processes Unlimited
- Lecture 09 – Markov Decision Processes (cont.) Unlimited
- Lecture 10 – Reinforcement Learning Unlimited
- Lecture 12 – Probability Unlimited
- Lecture 13 – Bayes’ Nets Unlimited
- Lecture 15 – Bayes’ Nets III: Inference Unlimited
- Lecture 16 – Bayes’ Nets IV: Sampling Unlimited
- Lecture 17 – Midterm Review Unlimited
- Lecture 18 – Decision Diagrams Unlimited
- Lecture 19 – Hidden Markov Models (HMMs): Intro and Filtering Unlimited
- Lecture 20 – HMMs: Particle Filtering Unlimited
- Lecture 22 – Machine Learning (ML): Naive Bayes Unlimited
- Lecture 23 – Perceptrons and More Unlimited
- Lecture 25 – Advanced Applications: Robotics/Vision/Language Unlimited
- Lecture 26 – Advanced Applications: Robotics/Vision/Language (cont.) Unlimited
- Lecture 27 – Conclusion Unlimited
