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Artificaial Intelligence (M-I-T)
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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Basic and Health Sciences
Biology
Chemistry
Mathematics
Physics
Medicine
Test Prep
Applied Sciences
Agricultural Science
Computer Science
Earth, Atmospheric, and Planetary Sciences
Energy
Engineering
Healthcare
Social Sciences
Business and Finance
Economics
English
History
Arts and Humanities
Law
Literature and Linguistics
Management
Marketing
Mass Communication
Philosophy
Physical Education
Political Science
Psychology
Sociology