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Regrading: If you feel that your homework was not graded correctly, you should return it to me, along with a written explanation of the error. I will evaluate this explanation and award any necessary points.


A Sample second midterm from last year can be found here . One thing to note: last year, we covered utility before the second midterm, so there are some utility questions on it. You can ignore those.

A version with answers can be found here.


11/8: Don't forget that the second midterm is next Monday. Here is a list of potential topics.

11/3: Here is the decision tree dataset from Wednesday's lecture.

10/23. I've typed up some sample FOL encodings and resolution proofs, both the examples from Wednesday's class, and also some additional problems. You can find the questions only here , and a version with the answers here . (try to do them yourself before looking at the answers!)


Sample midterm from last year can be found here .

A version with answers can be found here.

A list of potential midterm topics can be found here .


Midterm 1 solutions are here. Some statistics: High: 171, Low: 125, Mean: 149.2, median, 148, stDev: 11.3


Lectures and associated readings:

Date Topic Associated Reading Slides
August 30 Lecture 1: Introduction. What is AI? R & N: 1.1 pp 1-4, 26.1-2: pp 947-958 Full size Printable
September 1,8 Lecture 2a: Agents and Environments R & N: Chapter 2: pp 32-58 Full size Printable
September 1 Lecture 2b: Introduction to Python Python Tutorial Full size Printable
September 8 Lecture 2c: More Python Dive Into Python Full size Printable
September 13 Lecture 3: Uninformed Search R & N: 3.1-3.5 pp 59-83 Full size Printable
September 15 Lecture 4: Heuristic Search R & N: 4.1-4.2 pp 94-109 Full size Printable
September 20 Lecture 5: Constraint Satisfaction R & N: 5.1-5.2 pp 137-150 Full size Printable
September 22 Lecture 6: Project 1 discussion Project 1 Project 1
September 27 Lecture 7: Local Search and Genetic Algorithms R & N: 4.3 pp 110-119 Full size Printable
October 11 Lecture 8: Introduction to Knowledge Representation R & N: 7.1-7.5: pp 194-220 Full size Printable
October 13 Lecture 9: Introduction to First-order Logic R & N: 8.1-8.3: pp 240-258 Full size Printable
October 18 Lecture 10: Inference in First-order Logic R & N: 9.1-9.6: pp 272-300 Full size Printable
October 20 Lecture 11: Ontologies Protege Tutorial
Ontology Development 101
Full size Printable
November 1 Lecture 12: Decision Trees R & N 18.3: pp 653-663 Full size Printable
November 3 Lecture 13: Introduction to Probability R & N 13.1-13.6: pp 462-481 Full size Printable
November 8 Lecture 14: Belief Networks R & N 14.1-14.4: pp 492-510 Full size Printable
November 10 Lecture 15: Bayesian Learning R & N 20.1: pp 712-716 Full size Printable
November 17,22 Lecture 16: Making Decisions R & N 16.1-16.3, 16.6: pp 584-591,600-603
R & N 6.1-6.3 pp 161-171
Full size Printable
November 29 Project 3 discussion See class handout See Bayesian Learning slides
December 1 Neural Networks I R & N: 20.5: 736-748 Full size Printable
December 5 Neural Networks II, summary Full size Printable

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Sample code from the intro to python lecture:

Samples of python classes

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GA/GP readings:

Python links:

AIMA links