CSE 327 AI Theory and Practice, Spring 2012
Professor Jeff Heflin
MWF 1:10-2:00pm, Packard Lab 466
Check here for updates regarding the course.
- 4/30/12 - I have posted the study guide for the final. We will discuss this at the review session today. I also posted the solutions to HW #6.
- 4/20/12 - I will be presenting research outside of the country on April 23-27. There will be no office hours. We will have substitute lectures on Monday (computer vision), Wednesday (reinforcement learning) and Friday (contemporary methods for supervised learning). I will hold a review session on Monday, April 30 from 1:10-2pm in our regular room (Packard 466).
- 4/19/12 - Our final is scheduled for Wed., May 2 from 4-7pm in Neville 002.
- 4/13/12 - As mentioned in class, there are a few clarifications On HW #6. First, in prboelm #2 assume the a probability of e that the count is under by 1, and an additional e probability that it is over by one. Also, assume the off-by-one counts cannot be combined with an out-of-focus event. On problem #5, insufficient information is given to provide a CPT for variable B. You can simply omit that table, it is not needed to solve the problem.
- 3/13/12 - I will be out of town 3/14 and 3/15. You will have a substitute professor for class on 3/14. Office hours on 3/15 are canceled. I will hold additional hours on 3/16 from 2:10-3pm. I have posted the solutions to HW #3
- 02/24/12 - I have posted the solutions to HW #2 and a study guide for the midterm.
- 02/13/12 - I have posted the solutions to HW #1
- 02/10/12 - The original handout for Homework #2 should say Chapters 3 and 5, not 4 and 6. The online version has been corrected.
This course will provide a general introduction to Artificial Intelligence(AI). We will discuss what AI is, survey some of the major results in the field, and look at a few promising directions. In particular, we will seek answers to questions such as:
Our examination of these problems will focus on various data structures and algorithms that have been proposed as solutions.
- How do you represent and reason with general-purpose knowledge?
- How can a robot or artificial agent formulate a plan to achieve a task?
- How can an agent make good decisions given uncertainty about its environment?
- How can an agent learn in order to improve its behavior or cope with unanticipated situations?
For details about course content, grading, and assignments, see the class syllabus.
Russell, Stuart and Peter Norvig, Artificial Intelligence: A Modern Approach (third edition). Prentice-Hall, New Jersey, 2010. ISBN 0-13-604259-7
Mon. 10-11:30am, Thr. 1:30-3pm and by appointment in Packard Lab 330
Each of the homeworks will be made available here after they are
handed out in class. The online versions of the homework are in PDF format.
Your readings will be listed below as they are assigned. Unless otherwise specified, all readings are from our textbook, Artificial Intelligence: A Modern Approach.
|Read Ch. 1 (pp. 1-30)||1/18|
|Read Sect. 2.1-2.3 (pp. 34-46)||1/20|
|Read Sect. 2.4-2.5 (pp. 46-59)||1/23|
|Read Sect. 3.1-3.3 (pp. 64-81)||1/25|
|Read Sect. 3.4 (pp. 81-91)||1/27|
|Read Sect. 3.5 (pp. 92-102)||1/30|
|Read Sect. 3.6-3.7 (pp. 102-109)||2/1|
|Read Sect. 5.1-5.2 (pp. 161-167)||2/3|
|Read Sect. 5.4 (pp. 171-176)||2/6|
|Read Sect. 5.7-5.9, 7.1-7.2 (pp. 180-186,234-240)||2/8|
|Read Sect. 7.3-7.4 (pp. 240-249)||2/10|
|Read Sect. 7.5.3-7.5.4, 7.7-7.8 (pp. 256-259, 265-275)||2/13|
|Read Sect. 8.1-8.2.2 (pp. 285-294)||2/15|
|Read Sect. 8.2.3-8.2.8 (pp. 294-300)||2/17|
|Read Sect. 8.3-8.5 (pp. 300-313)||2/20|
|Read An Introduction to Prolog Programming by Ulle Endriss, Chapter 1 (pp. 1-12)||2/22|
|Read Sect. 9.1-9.2 (pp. 322-329)||2/24|
|Read Sect. 9.4 (pp. 337-345)||2/27|
|Read Sect. 12.1-12.2, 12.5, 12.7-12.8 (pp. 437-445, 453-458, 462-468)||3/2
|Read Sect. 10.1 (pp. 366-372)||3/12|
|Read Sect. 10.2 (pp. 373-379)||3/14|
|Read Sect. 10.3 (pp. 397-387)||3/19|
|Read Sect. 13.1-13.2 (pp. 480-490)||3/23|
|Read Sect. 13.3-13.7 (pp. 490-503)||3/26|
|Read Sect. 14.1 (pp. 510-513)||3/28|
|Read Sect. 14.2 (pp. 513-518)||3/30|
|Read Sect. 14.4 (pp. 522-530)||4/2|
|Read Sect. 15.1-15.3 (pp. 566-583)||4/4|
|Read Sect. 16.1-16.3 (pp. 610-621)||4/6|
|Read Sect. 18.1-18.2 (pp. 693-697)||4/9|
|Read Sect. 18.3 (pp. 697-707)||4/11|
|Read Sect. 18.4 (pp. 708-713)||4/13|
|Read Sect. 18.7.1-18.7.2 (pp. 727-731)||4/16|
|Read Sect. 18.7.3-18.7.5 (pp. 731-737)||4/18|
|Read Sect. 19.1 (pp. 768 - 776)||4/20|
|Read Sect. 24.1-24.3 (pp. 928-947)||4/23|
|Read Reinforcement Learning: An Introduction, Chapter 1 by Sutton and Barto||4/25|
Additional Class Materials
- Contains information on course content, grading, assignments, and office
- Supplemental Slides
- This directory contains the slides that I use in class. Note, these slides only cover part of the lecture, and should not be used as a substitute for it.
- Search Strategy Code
- A ZIP file containing Java classes that implement three different best-first search strategies. The code is designed to be extended with definitions of specific search problems, so that it can then be used to solve those problems. This code should be used when performing the Extra Credit exercise of HW #2. This code is intended only for use in conjunction with CSE 327 at Lehigh, and is not authorized for any other purpose.
- An Introduction to Prolog Programming by Ulle Endriss
- Gives a light weight introduction to Prolog syntax, queries, and style.
- Midterm Study Guide
- This document briefly discusses the format of the test, and provides a partial list of topics you need to know for the test. It also explicitly lists topics You do not need to know.
- SWI-Prolog is free software. If you are using a personal machine, you can download SWI-Prolog from the web page listed below. If you are in a Lehigh computer lab, you should install it via the Lehigh Software page. A link to the online reference manual is also provided.
- Sample Prolog programs
- These are the examples that were shown in class
- Final Study Guide
- This document briefly discusses the format of the final, and provides a partial list of topics you need to know for the test. It also explicitly lists topics you do not need to know.