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Course6.03430 lectures24 h 18 min

Artificial Intelligence

Patrick Henry Winston · Massachusetts Institute of Technology

Start with lecture 1 At MIT OpenCourseWare

About

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.

Lectures

Lecture Videos

  1. Lecture 1: Introduction and Scope 48 min · Video
  2. Lecture 2: Reasoning: Goal Trees and Problem Solving 46 min · Video
  3. Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems 50 min · Video
  4. Lecture 4: Search: Depth-First, Hill Climbing, Beam 49 min · Video
  5. Lecture 5: Search: Optimal, Branch and Bound, A* 49 min · Video
  6. Lecture 6: Search: Games, Minimax, and Alpha-Beta 49 min · Video
  7. Lecture 7: Constraints: Interpreting Line Drawings 50 min · Video
  8. Lecture 8: Constraints: Search, Domain Reduction 46 min · Video
  9. Lecture 9: Constraints: Visual Object Recognition 52 min · Video
  10. Lecture 10: Introduction to Learning, Nearest Neighbors 50 min · Video
  11. Lecture 11: Learning: Identification Trees, Disorder 50 min · Video
  12. Lecture 12A: Neural Nets 51 min · Video
  13. Lecture 12B: Deep Neural Nets 50 min · Video
  14. Lecture 13: Learning: Genetic Algorithms 48 min · Video
  15. Lecture 14: Learning: Sparse Spaces, Phonology 48 min · Video
  16. Lecture 15: Learning: Near Misses, Felicity Conditions 47 min · Video
  17. Lecture 16: Learning: Support Vector Machines 50 min · Video
  18. Lecture 17: Learning: Boosting 52 min · Video
  19. Lecture 18: Representations: Classes, Trajectories, Transitions 49 min · Video
  20. Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind 50 min · Video
  21. Lecture 21: Probabilistic Inference I 49 min · Video
  22. Lecture 22: Probabilistic Inference II 49 min · Video
  23. Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary 50 min · Video

Mega-Recitation Videos

  1. Mega-Recitation 1: Rule-Based Systems 47 min · Video
  2. Mega-Recitation 2: Basic Search, Optimal Search 52 min · Video
  3. Mega-Recitation 3: Games, Minimax, Alpha-Beta 51 min · Video
  4. Mega-Recitation 4: Neural Nets 53 min · Video
  5. Mega-Recitation 5: Support Vector Machines 50 min · Video
  6. Mega-Recitation 6: Boosting 50 min · Video
  7. Mega-Recitation 7: Near Misses, Arch Learning 34 min · Video

Syllabus and materials

At MIT OpenCourseWare, as the course publishes them.

Credits and licence

Institution
Massachusetts Institute of Technology
Department
Electrical Engineering and Computer Science
Teacher
Patrick Henry Winston
Term
Fall 2010
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
MIT OpenCourseWare
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only. MIT OpenCourseWare's terms
Attribution
Patrick Henry Winston. Artificial Intelligence. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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