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15-381 Artificial Intelligence, Spring 2006

Martial Hebert

School of Computer Science, Carnegie Mellon University

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Homeworks  Review Sessions Syllabus 

This class is all about autonomy: how can machines make their own decisions and make them well? The goal is that by the end of the class you will be an expert in a wide range of useful technologies for automated decision making. You'll have seen practical examples of putting AI into commercial, scientific, federal and consumer applications. And hopefully you'll be ready to find new AI technologies and applications.

We will not stick strictly to the traditional definitions of AI, but will cover technologies from a wider range of disciplines that your instructor believes are most important for practical autonomous decision-making.

Incoming students need to be proficient with the theory and practice of algorithms and programming: 15-212 is a prerequisite.

Class lectures: Tuesdays & Thursdays 1.30pm-2:50pm, Wean Hall 7500 starting on Tuesday January 17th, 2006

Instructor:

Teaching Assistants:

  • Sonia Chernova(soniac@cs.cmu.edu), WeH 1313, x8-2601, Office hours: Thu. 4-6pm
  • Sajid Siddiqi(siddiqi@cs.cmu.edu), NSH 3122, x8-6014, Office hours: Wed. 4-6pm
  • Vaibhav Mehta(vaibhav+@cs.cmu.edu), WeH 8303, x8-2993, Office hours: Tue. 5:30-7:30pm
  • Rong Yan(yanrong@cs.cmu.edu), NSH 4533, x8-9515, Office hours: Mon. 4-6pm

    Note: if you find the doors in NSH are locked after 5pm, please contact the TAs via phone so that we can open the door for you.

       

Textbook:

Grading:

  • Final grades will be based on midterm (20%), homework (40%), and final exam (40%)

Policy on late homework:

Homework must be handed in at the start of class (1.30pm) on the due date.

  • If it is between 0-24 hours late it will receive 90% of its score.
  • If it is between 24-48 hours late it will receive 50% of its score.
  • If it is more than 48 hours late it will receive no score.

Policy on collaboration:

You are encouraged to discuss the general algorithms and ideas in the class in order to help each other answer homework questions. You are also welcome to give each other examples that are not on the assignment in order to demonstrate how to solve problems. But we require you to

  • not explicitly tell each other the answers
  • not to copy answers
  • not to allow your answers to be copied

This policy is in order to be fair to the rest of the students in the class. We will have a grading policy of watching for cheating and we will follow up if it is detected. Some assignments will allow you to form teams of two people. In that case you will submit one paper on behalf of the partnership, and partners may explicitly help each other out with answers, but the above anti-copying rules still apply between teams.

Review Sessions

Day & Time:

Tuesday 6:00pm-8:00pm

Location :

WeH 5409

Date:

Announced in class and on the website


There will be occasional optional review sessions on Tuesday 6:00pm-8:00pm in WeH 5409, often used for things like Q&A for assignments and reviewing material for exams. Please check the website for information on upcoming review sessions.
 

Homework assignments

Assignments will be available for downloading on the specified dates.

 

Links

Out

Return

Solutions

HW1

hw1.pdf, hw1.ps, puzzles.tar.gz

Tue Jan. 24

Tue Feb. 7

 hw1sol.pdf, hw1sol.ps

HW2

hw2.pdf, hw2.ps, puzzles.tar.gz

Tue Feb. 7

Tue Feb. 21

 hw2sol.pdf, hw2sol.ps

HW3

hw3.pdf, hw3.ps

Tue Feb. 21

Tue Mar. 7

 hw3sol.pdf

HW4

hw4.pdf, hw4.tar.gz 

Tue Mar. 7

Thu Mar. 30

 hw4sol.pdf

HW5

hw5.pdf 

Thu Mar. 30

Thu Apr. 13

 hw5sol.pdf

HW6

hw6.pdf

Tue Apr. 17

Tue May 2

 hw6sol.pdf

Syllabus

Date

Topic

Chapter

Notes

Links/Slides

 

 

 

 

 

Jan. 17

Intro

 

 

Introduction

 

SEARCH

 

 

 

Jan. 19

Search

3

 

Uninformed Search
Informed Search

Jan. 24

Search: Hill Climbing, Stochastic Search, Simulated Annealing

3,4

HW1 out

Local/Stochastic Search 

Jan. 26

Search: Hill Climbing, Stochastic Search, Simulated Annealing

3,4

 

 

Jan. 31

Constraint Satisfaction Problems

5

HW1 Review

CSP

Feb. 2

Constraint Satisfaction Problems

5

 

 

Feb. 7

Robot Motion Planning

25

HW1 due; HW2 out

Motion Planning

Feb. 9

Robot Motion Planning

25

 

 

 

GAMES

 

 

 

Feb. 14

Guest Lecturer: Stephen Smith

25

HW2 Review

 

Feb. 16

Algorithms for Playing and Solving Games

6

 

Games

Feb. 21

Games with Hidden Information

6

HW2 due; HW3 out

GameII

Feb. 23

Non-Zero-Sum Games

6

 

GameIII 

Feb. 28

Auctions and Negotiations

6

HW3 Review

GameIV 

 

REASONING

 

 

 

Mar. 2

Automated Theorem Proving with Propositional Logic

8,9

 

Reasoning 

Mar. 7

Reasoning, Cont.

11

HW3 due; HW4 out

Planning 

Mar. 9

Midterm Exam

 

Midterm

Previous Exam:

S05,  S05-sols
S04,  S04-sols
S02,  S02-sols

Review Slides:
Review.pdf

Midterm Solution:

Midterm06sol

Mar. 14/16

Midterm Break

 

 

 

 

LEARNING

 

 

 

Mar. 21

Intro: Uncertainty and Probabilistic Learning

18

 

LearningIntro

Mar. 23

Decision Trees

18

 

DecisionTree 

Mar. 28

Decision Trees (cont.)

18

HW4 Review

DecisionTreeII 

Mar. 30

K-Means

20

HW4 due; HW5 out

Cluster 

Apr. 4

Neural Networks

20

 

NeuralNetwork 

Apr. 6

Neural Networks

20

HW5 Review

 

Apr. 11

Cross-Validation & Intro Bayes Net

14, 20

 

CV+Bayes

 

REASONING WITH UNCERTAINTY

 

 

 

Apr. 13

Bayes Nets

14

HW5 due;

Bayesn2 

Apr. 18

Bayes Nets

14

HW6 out

Bayesn3 

Apr. 20

No Class

 

 

 

Apr. 25

Markov Decision Processes

16,17

HW6 Review

MDP 

Apr. 27

Markov Decision Processes

16,17

 

 

May 2

Reinforcement Learning

21

HW6 due 

RL 

May 4

Reinforcement Learning

21

 

 

May 12

Final Exam

 

8:30a.m.-11:30a.m.
HH B103 & HH B131

Previous Exam:

S05-sols, S04-sols

S03-sols, S02-sols
Review:

Partial Review.pdf

More Review.pdf