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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
- What is Artificial Intelligence (AI)? Unlimited
- Problem Solving and Search Unlimited
- Logic Unlimited
- Satisfiability and Validity Unlimited
- First-Order Logic Unlimited
- Resolution Theorem Proving: Propositional Logic Unlimited
- Resolution Theorem Proving: First Order Logic Unlimited
- Logic Miscellanea Unlimited
- Planning Unlimited
- Partial-Order Planning Algorithms Unlimited
- Graph Plan Unlimited
- Planning Miscellany Unlimited
- Probability Unlimited
- Bayesian Networks Unlimited
- Inference in Bayesian Networks Unlimited
- Where do Bayesian Networks Come From? Unlimited
- Learning With Hidden Variables Unlimited
- Decision Making under Uncertainty Unlimited
- Markov Decision Processes Unlimited
- Reinforcement Learning Unlimited
Instructor
Massachusetts Institute of Technology
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