cs 15 381 artificial intelligence representation and problem solving n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
CS 15-381: Artificial Intelligence: Representation and Problem Solving PowerPoint Presentation
Download Presentation
CS 15-381: Artificial Intelligence: Representation and Problem Solving

Loading in 2 Seconds...

play fullscreen
1 / 10

CS 15-381: Artificial Intelligence: Representation and Problem Solving - PowerPoint PPT Presentation


  • 118 Views
  • Uploaded on

CS 15-381: Artificial Intelligence: Representation and Problem Solving. Fall 2002 Prof. Tuomas Sandholm Computer Science Department Carnegie Mellon University. Attendance list. Student name, id, email address. Main course topics. Problem representation & reasoning Search

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

CS 15-381: Artificial Intelligence: Representation and Problem Solving


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
    Presentation Transcript
    1. CS 15-381: Artificial Intelligence: Representation and Problem Solving Fall 2002 Prof. Tuomas Sandholm Computer Science Department Carnegie Mellon University

    2. Attendance list Student name, id, email address

    3. Main course topics • Problem representation & reasoning • Search • Constructive search & iterative refinement • For optimization problems & constraint satisfaction problems • Game playing • Logic • Propositional logic • First-order logic • Planning • Probabilistic reasoning & planning • Machine learning • Supervised learning (= learning from examples) • Reinforcement learning (= learning by reinforcement)

    4. Course personnel & times • Instructor • Prof. Tuomas Sandholm • Lectures TuTh 10:30-11:50 in Hammerschlag Hall B103 • Office hour Tu 12-1 in Wean Hall 4606 • TAs (published experts in AI): • Kate Larson • klarson@cs.cmu.edu • Wean Hall 3203 • Office hours Mo 2-3, We 5-6 • Pat Riley • pfr+@cs.cmu.edu • Wean Hall 7113 • Office hours We 1-2, Th 12-1

    5. Homeworks & grading • 4 programming homeworks • Can use any programming language • Recommended: Java (homework 2 must be in Java) • Evaluation: • Homeworks 45% • Midterm exam 25% • Final exam 30%

    6. Honor code • Strict honor code with severe punishment for violators • Assignments will specify whether or not teamwork is allowed • No downloading / copying of code or other answers is allowed • If you use a string of at least 5 words from some source, you must cite the source

    7. Before every lecture • Print out slides for that lecture from the course web page • so you can take notes on the slides in class

    8. Textbook • Artificial Intelligence: A Modern Approach • first edition • by Russell and Norvig • published by Prentice Hall

    9. Course web page • http://www.cs.cmu.edu/~sandholm/cs15-381 • Includes a course schedule • updated regularly • Homeworks will be posted via the web page

    10. Questions?