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

• Lecture 1: Introduction and Scope (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)
• Lecture 7: Constraints: Interpreting Line Drawings (M-I-T)
• Lecture 8: Constraints: Search, Domain Reduction (M-I-T)
• Lecture 9: Constraints: Visual Object Recognition (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 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)