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6.825 is a graduate-level introduction to artificial intelligence.
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English [CC]
FREE
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Description
Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).
Course content
- Lecture 1: What is Artificial Intelligence (AI)? Unlimited
- Lecture 2: Problem Solving and Search Unlimited
- Lecture 3: Logic Unlimited
- Lecture 4.: Satisfiability and Validity Unlimited
- Lecture 5.: First-Order Logic Unlimited
- Lecture 7.: Resolution Theorem Proving: Propositional Logic Unlimited
- Lecture 8.: Resolution Theorem Proving: First Order Logic Unlimited
- Lecture 9: Logic Miscellanea Unlimited
- Lecture 10: Planning Unlimited
- Lecture 11: Partial-Order Planning Algorithms Unlimited
- Lecture 12: Graph Plan Unlimited
- Lecture 13: Planning Miscellany Unlimited
- Lecture 14: Probability Unlimited
- Lecture 15: Bayesian Networks Unlimited
- Lecture 16: Inference in Bayesian Networks Unlimited
- Lecture 17: Where do Bayesian Networks Come From? Unlimited
- Lecture 18: Learning With Hidden Variables Unlimited
- Lecture 19: Decision Making under Uncertainty Unlimited
- Lecture 20: Markov Decision Processes Unlimited
- Lecture 22: Reinforcement Learning Unlimited
Instructor
Massachusetts Institute of Technology
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