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Last updated:

September 26, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

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Description

CS 188: Artificial Intelligence (Fall 2011, UC Berkeley). Instructor: Professor Dan Klein. This course introduces

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 Curriculum

  • 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

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