SCCI Digital Library

Artificaial Intelligence (M-I-T)

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