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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

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CC BY-NC-SA 4.0

Course introduction

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.

Course Information

Source	MIT 開放式課程
Department	Electrical Engineering and Computer Science
Language	English
Number of videos	30

Course videos (30)

1. Introduction and Scope

1. Introduction and Scope

2. Reasoning: Goal Trees and Problem Solving

2. Reasoning: Goal Trees and Problem Solving

3. Reasoning: Goal Trees and Rule-Based Expert Systems

3. Reasoning: Goal Trees and Rule-Based Expert Systems

4. Search: Depth-First, Hill Climbing, Beam

4. Search: Depth-First, Hill Climbing, Beam

5. Search: Optimal, Branch and Bound, A*

5. Search: Optimal, Branch and Bound, A*

6. Search: Games, Minimax, and Alpha-Beta

6. Search: Games, Minimax, and Alpha-Beta

7. Constraints: Interpreting Line Drawings

7. Constraints: Interpreting Line Drawings

8. Constraints: Search, Domain Reduction

8. Constraints: Search, Domain Reduction

9. Constraints: Visual Object Recognition

9. Constraints: Visual Object Recognition

10. Introduction to Learning, Nearest Neighbors

10. Introduction to Learning, Nearest Neighbors

11. Learning: Identification Trees, Disorder

11. Learning: Identification Trees, Disorder

12a: Neural Nets

12a: Neural Nets

12b: Deep Neural Nets

12b: Deep Neural Nets

13. Learning: Genetic Algorithms

13. Learning: Genetic Algorithms

14. Learning: Sparse Spaces, Phonology

14. Learning: Sparse Spaces, Phonology

15. Learning: Near Misses, Felicity Conditions

15. Learning: Near Misses, Felicity Conditions

16. Learning: Support Vector Machines

16. Learning: Support Vector Machines

17. Learning: Boosting

17. Learning: Boosting

18. Representations: Classes, Trajectories, Transitions

18. Representations: Classes, Trajectories, Transitions

19. Architectures: GPS, SOAR, Subsumption, Society of Mind

19. Architectures: GPS, SOAR, Subsumption, Society of Mind

21. Probabilistic Inference I

21. Probabilistic Inference I

22. Probabilistic Inference II

22. Probabilistic Inference II

23. Model Merging, Cross-Modal Coupling, Course Summary

23. Model Merging, Cross-Modal Coupling, Course Summary

Mega-R1. Rule-Based Systems

Mega-R1. Rule-Based Systems

Mega-R2. Basic Search, Optimal Search

Mega-R2. Basic Search, Optimal Search

Mega-R3. Games, Minimax, Alpha-Beta

Mega-R3. Games, Minimax, Alpha-Beta

Mega-R4. Neural Nets

Mega-R4. Neural Nets

Mega-R5. Support Vector Machines

Mega-R5. Support Vector Machines

Mega-R6. Boosting

Mega-R6. Boosting

Mega-R7. Near Misses, Arch Learning

Mega-R7. Near Misses, Arch Learning