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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Artificial Intelligence (Fall 2010) (M-I-T) » Lecture 21: Probabilistic Inference I (M-I-T)

Lecture 21: Probabilistic Inference I (M-I-T)

Course:Artificial Intelligence (Fall 2010) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Patrick Henry Winston
Level:Undergraduate

Artificial Intelligence (Fall 2010) (M-I-T)

Lecture 10: Introduction to Learning, Nearest Neighbors (M-I-T)
Lecture 11: Learning: Identification Trees, Disorder (M-I-T)
Lecture 12A: Neural Nets (M-I-T)
Lecture 12B: Deep Neural Nets (M-I-T)
Lecture 13: Learning: Genetic Algorithms (M-I-T)
Lecture 14: Learning: Sparse Spaces, Phonology (M-I-T)
Lecture 15: Learning: Near Misses, Felicity Conditions (M-I-T)
Lecture 16: Learning: Support Vector Machines (M-I-T)
Lecture 17: Learning: Boosting (M-I-T)
Lecture 18: Representations: Classes, Trajectories, Transitions (M-I-T)
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind (M-I-T)
Lecture 1: Introduction and Scope (M-I-T)
Lecture 21: Probabilistic Inference I (M-I-T)
Lecture 22: Probabilistic Inference II (M-I-T)
Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary (M-I-T)
Lecture 2: Reasoning: Goal Trees and Problem Solving (M-I-T)
Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems (M-I-T)
Lecture 4: Search: Depth-First, Hill Climbing, Beam (M-I-T)
Lecture 5: Search: Optimal, Branch and Bound, A* (M-I-T)
Lecture 6: Search: Games, Minimax, and Alpha-Beta (M-I-T)

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