Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Massachusetts Institute of Technology

Artificial Intelligence (Fall 2010)

Massachusetts Institute of Technology via MIT OpenCourseWare

Overview

Course Features
  • Video lectures
  • Captions/transcript
  • Assignments: programming (no examples)
  • Exams (no solutions)
  • Recitation videos
Educator Features
  • Instructor insights
  • Teaching assistant insights
Course Highlights

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

Course Description

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.

Syllabus

1. Introduction and Scope.
2. Reasoning: Goal Trees and Problem Solving.
3. Reasoning: Goal Trees and Rule-Based Expert Systems.
4. Search: Depth-First, Hill Climbing, Beam.
5. Search: Optimal, Branch and Bound, A*.
6. Search: Games, Minimax, and Alpha-Beta.
7. Constraints: Interpreting Line Drawings.
8. Constraints: Search, Domain Reduction.
9. Constraints: Visual Object Recognition.
10. Introduction to Learning, Nearest Neighbors.
11. Learning: Identification Trees, Disorder.
12a: Neural Nets.
12b: Deep Neural Nets.
13. Learning: Genetic Algorithms.
14. Learning: Sparse Spaces, Phonology.
15. Learning: Near Misses, Felicity Conditions.
16. Learning: Support Vector Machines.
17. Learning: Boosting.
18. Representations: Classes, Trajectories, Transitions.
19. Architectures: GPS, SOAR, Subsumption, Society of Mind.
21. Probabilistic Inference I.
22. Probabilistic Inference II.
23. Model Merging, Cross-Modal Coupling, Course Summary.
Mega-R1. Rule-Based Systems.
Mega-R2. Basic Search, Optimal Search.
Mega-R3. Games, Minimax, Alpha-Beta.
Mega-R4. Neural Nets.
Mega-R5. Support Vector Machines.
Mega-R6. Boosting.
Mega-R7. Near Misses, Arch Learning.

Taught by

Prof. Patrick Henry Winston

Reviews

5.0 rating, based on 2 Class Central reviews

Start your review of Artificial Intelligence (Fall 2010)

  • Profile image for Littin Rajan
    Littin Rajan
    It was so cool. Learn most out of it. It tecaches most areas of Artificial Intelligence with real-life scenarios
  • Ajina Fathima
    Its a really indepth knowledgeable class
    You can add more quizz
    You can add more interactive sections

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.