MIT · 6.034

Artificial Intelligence

人工智能

Foundational methods for reasoning, learning, search, and knowledge representation.

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Lecture 1: Introduction and scope

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Subject：Artificial Intelligence

Level：Undergraduate

Suggested stage：Years 2–3 (inferred)

Public-material edition：2010

Language：English

Videos：Available

Assignments / solutions：Available / Not verified

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▶Lectures, video & readingCourse content in the official sequence23

Lecture 1: Introduction and scope Lecture 2: Goal trees and problem solving Lecture 3: Rule-based expert systems Lecture 4: Depth-first, hill climbing, and beam search Lecture 5: Optimal search, branch and bound, and A*Lecture 6: Games, minimax, and alpha-beta Lecture 7: Line-drawing constraints Lecture 8: Constraint search and domain reduction Lecture 9: Visual object recognition Lecture 10: Learning and nearest neighbors Lecture 11: Identification trees Lecture 12: Neural networks Lecture 13: Deep neural networks Lecture 14: Genetic algorithms Lecture 15: Sparse spaces and phonology Lecture 16: Near misses and felicity conditions Lecture 17: Support vector machines Lecture 18: Boosting Lecture 19: Classes, trajectories, and transitions Lecture 20: AI architectures Lecture 21: Probabilistic inference I Lecture 22: Probabilistic inference II Lecture 23: Model merging and course summary

✓Assignments & practiceOpen the matching problem or assignment page6

Problem Set 0 Problem Set 1 Problem Set 2 Problem Set 3 Problem Set 4 Problem Set 5

◇Exams & quizzesOfficial quizzes, exams and review material5

Quiz 1 Quiz 2 Quiz 3 Quiz 4 Final exam

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