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Uedu Open / Artificial Intelligence
6.034

Artificial Intelligence

Patrick Henry Winston | Fall 2010
Data Science, Analytics & Computer Technology AI Algorithms and Data Structures Machine Learning Computer Science Engineering Artificial Intelligence Theory of Computation
前往原始課程
CC BY-NC-SA 4.0
課程簡介
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
課程資訊
來源MIT 開放式課程
科系Electrical Engineering and Computer Science
語言English
影片數30
課程影片 (30)
1
1. Introduction and Scope
1. Introduction and Scope
2
2. Reasoning: Goal Trees and Problem Solving
2. Reasoning: Goal Trees and Problem Solving
3
3. Reasoning: Goal Trees and Rule-Based Expert Systems
3. Reasoning: Goal Trees and Rule-Based Expert Systems
4
4. Search: Depth-First, Hill Climbing, Beam
4. Search: Depth-First, Hill Climbing, Beam
5
5. Search: Optimal, Branch and Bound, A*
5. Search: Optimal, Branch and Bound, A*
6
6. Search: Games, Minimax, and Alpha-Beta
6. Search: Games, Minimax, and Alpha-Beta
7
7. Constraints: Interpreting Line Drawings
7. Constraints: Interpreting Line Drawings
8
8. Constraints: Search, Domain Reduction
8. Constraints: Search, Domain Reduction
9
9. Constraints: Visual Object Recognition
9. Constraints: Visual Object Recognition
10
10. Introduction to Learning, Nearest Neighbors
10. Introduction to Learning, Nearest Neighbors
11
11. Learning: Identification Trees, Disorder
11. Learning: Identification Trees, Disorder
12
12a: Neural Nets
12a: Neural Nets
13
12b: Deep Neural Nets
12b: Deep Neural Nets
14
13. Learning: Genetic Algorithms
13. Learning: Genetic Algorithms
15
14. Learning: Sparse Spaces, Phonology
14. Learning: Sparse Spaces, Phonology
16
15. Learning: Near Misses, Felicity Conditions
15. Learning: Near Misses, Felicity Conditions
17
16. Learning: Support Vector Machines
16. Learning: Support Vector Machines
18
17. Learning: Boosting
17. Learning: Boosting
19
18. Representations: Classes, Trajectories, Transitions
18. Representations: Classes, Trajectories, Transitions
20
19. Architectures: GPS, SOAR, Subsumption, Society of Mind
19. Architectures: GPS, SOAR, Subsumption, Society of Mind
21
21. Probabilistic Inference I
21. Probabilistic Inference I
22
22. Probabilistic Inference II
22. Probabilistic Inference II
23
23. Model Merging, Cross-Modal Coupling, Course Summary
23. Model Merging, Cross-Modal Coupling, Course Summary
24
Mega-R1. Rule-Based Systems
Mega-R1. Rule-Based Systems
25
Mega-R2. Basic Search, Optimal Search
Mega-R2. Basic Search, Optimal Search
26
Mega-R3. Games, Minimax, Alpha-Beta
Mega-R3. Games, Minimax, Alpha-Beta
27
Mega-R4. Neural Nets
Mega-R4. Neural Nets
28
Mega-R5. Support Vector Machines
Mega-R5. Support Vector Machines
29
Mega-R6. Boosting
Mega-R6. Boosting
30
Mega-R7. Near Misses, Arch Learning
Mega-R7. Near Misses, Arch Learning