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Artificaial Intelligence (M-I-T)

(30 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • 1. Introduction and Scope (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
2
  • 10. Introduction to Learning, Nearest Neighbors (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
3
  • 11. Learning: Identification Trees, Disorder (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
4
  • 12a: Neural Nets (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
5
  • 12b: Deep Neural Nets (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
6
  • 13. Learning: Genetic Algorithms (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
7
  • 14. Learning: Sparse Spaces, Phonology (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
8
  • 15. Learning: Near Misses, Felicity Conditions (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
9
  • 16. Learning: Support Vector Machines (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
10
  • 17. Learning: Boosting (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
11
  • 18. Representations: Classes, Trajectories, Transitions (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
12
  • 19. Architectures: GPS, SOAR, Subsumption, Society of Mind (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
13
  • 2. Reasoning: Goal Trees and Problem Solving (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
14
  • 21. Probabilistic Inference I (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
15
  • 22. Probabilistic Inference II (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
16
  • 23. Model Merging, Cross-Modal Coupling, Course Summary (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
17
  • 3. Reasoning: Goal Trees and Rule-Based Expert Systems (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
18
  • 4. Search: Depth-First, Hill Climbing, Beam (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
19
  • 5. Search: Optimal, Branch and Bound, A* (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
20
  • 6. Search: Games, Minimax, and Alpha-Beta (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
21
  • 7. Constraints: Interpreting Line Drawings (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
22
  • 8. Constraints: Search, Domain Reduction (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
23
  • 9. Constraints: Visual Object Recognition (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
24
  • Mega-R1. Rule-Based Systems (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences
25
  • Mega-R2. Basic Search, Optimal Search (M-I-T)
Artificaial Intelligence (M-I-T) MIT Prof. Mark Seifter Applied Sciences

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