MIT OpenCourseWare

Browse materialsMIT 6.034 · Fall 2010

Artificial Intelligence

Subject: Artificial Intelligence

Art: not stated, Fletcher's Combined Block Telegraph Apparatus and Bell, 1889 · Public domain

Instructor: Patrick Henry Winston
Term: Fall 2010
Level: Undergraduate
What's inside: 30 videos · 6 problem sets · 16 exams · 1 reading · 51 other files · 12 web pages
Materials open: Videos here · files on MIT OCW

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6.034 · Fall 2010 · Study here (this version)
6.034 · Spring 2005 · Study here

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About this course

Source: MIT OpenCourseWare 6.034 Artificial Intelligence, Fall 2010, Patrick Henry Winston — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

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MIT's page format for this item wasn't recognized clearly enough to place it safely. (18 items)
Referenced in a table, but without MIT's own verified order, so it isn't used to claim a sequence.

Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Readings

Mega-Recitation Videos

7 videos

Tutorials

Assignments

12 files

Exams

20 files

Demonstrations

run the demonstrations (JNLP)JNLP file Download

Other materials from this course

30 video transcripts

2 images

MIT OpenCourseWare, Patrick Henry Winston, 6.034 Artificial Intelligence, Fall 2010.

License: CC BY-NC-SA 4.0 · Edition: Fall 2010 · Open the original course on OCW ↗

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Artificial Intelligence

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Mega-Recitation Videos

Tutorials

Assignments

Exams

Demonstrations

Other materials from this course

Lecture 1: Introduction and Scope

Lecture 2: Reasoning: Goal Trees and Problem Solving

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

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

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

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

Lecture 7: Constraints: Interpreting Line Drawings

Lecture 8: Constraints: Search, Domain Reduction

Lecture 9: Constraints: Visual Object Recognition

Lecture 10: Introduction to Learning, Nearest Neighbors

Lecture 11: Learning: Identification Trees, Disorder

Lecture 12A: Neural Nets

Lecture 12B: Deep Neural Nets

Lecture 13: Learning: Genetic Algorithms

Lecture 14: Learning: Sparse Spaces, Phonology

Lecture 15: Learning: Near Misses, Felicity Conditions

Lecture 16: Learning: Support Vector Machines

Lecture 17: Learning: Boosting

Lecture 18: Representations: Classes, Trajectories, Transitions

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

Lecture 21: Probabilistic Inference I

Lecture 22: Probabilistic Inference II

Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary