MIT 6.034 Artificial Intelligence, Fall 2010 (English)

   Wednesday, December 18, 2024   

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Lecture 1: 1. Introduction and Scope

Lecture 2: 2. Reasoning: Goal Trees and Problem Solving

Lecture 3: 3. Reasoning: Goal Trees and Rule-Based Expert Systems

Lecture 4: 4. Search: Depth-First, Hill Climbing, Beam

Lecture 5: 5. Search: Optimal, Branch and Bound, A*

Lecture 6: 6. Search: Games, Minimax, and Alpha-Beta

Lecture 7: 7. Constraints: Interpreting Line Drawings

Lecture 8: 8. Constraints: Search, Domain Reduction

Lecture 9: 9. Constraints: Visual Object Recognition

Lecture 10: 10. Introduction to Learning, Nearest Neighbors

Lecture 11: 11. Learning: Identification Trees, Disorder

Lecture 12: 12a: Neural Nets

Lecture 13: 12b: Deep Neural Nets

Lecture 14: 13. Learning: Genetic Algorithms

Lecture 15: 14. Learning: Sparse Spaces, Phonology

Lecture 16: 15. Learning: Near Misses, Felicity Conditions

Lecture 17: 16. Learning: Support Vector Machines

Lecture 18: 17. Learning: Boosting

Lecture 19: 18. Representations: Classes, Trajectories, Transitions

Lecture 20: 19. Architectures: GPS, SOAR, Subsumption, Society of Mind

Lecture 21: 21. Probabilistic Inference I

Lecture 22: 22. Probabilistic Inference II

Lecture 23: 23. Model Merging, Cross-Modal Coupling, Course Summary

Lecture 24: Mega-R1. Rule-Based Systems

Lecture 25: Mega-R2. Basic Search, Optimal Search

Lecture 26: Mega-R3. Games, Minimax, Alpha-Beta

Lecture 27: Mega-R4. Neural Nets

Lecture 28: Mega-R5. Support Vector Machines

Lecture 29: Mega-R6. Boosting

Lecture 30: Mega-R7. Near Misses, Arch Learning