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Home » Applied Sciences » Computer Science » Artificaial Intelligence (M-I-T) » 13. Learning: Genetic Algorithms (M-I-T)

13. Learning: Genetic Algorithms (M-I-T)

Course:Artificaial Intelligence (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Mark Seifter
Level:Undergraduate

Artificaial Intelligence (M-I-T)

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

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