CS 580 - Spring 2015

Intro Artificial Intelligence CRN:11836 CS 580 - 001

Instructor:  Prof. Harry Wechsler wechsler@gmu.edu

Email correspondence: from GMU accounts with subject: CS 580

Course Description Principles and methods for knowledge representation, reasoning, problem solving, planning, heuristic search, reasoning, learning, probabilistic reasoning, and natural language processing and their application to building intelligent systems in a variety of domains. LISP, PROLOG, MATLAB, or

expert system programming language.

 

Prereq: CS 310 and CS 330

 

Main Topics: Problem Solving, Search, Knowledge and Reasoning, Uncertainty and Probabilistic Reasoning, Learning, and Communication (Perception / Vision and Natural Language Processing). Additional topics time permitting: Data Mining, Deep Learning, and Biometrics.

 

Time, Day, and Venue: R Thursday, 4:30 pm - 7:10 pm

Innovation Hall 134

Office Hours: R Thursday, 3:15 4:15 pm or by appointment, ENGR 4448.

http://registrar.gmu.edu/calendars/spring-2015/

First day of classes: Thursday, January 22

Spring break: no class on Thursday, March 12

Last day of classes: Thursday, April 30

http://registrar.gmu.edu/calendars/spring-2015/final-exam/

FINAL Exam: Thursday, May 7, 4:30 7:15 pm

Textbook: Artificial Intelligence: A Modern Approach, Russell and Norvig (3rd ed.), Prentice Hall, 2010.
 
Textbook Website: http://aima.cs.berkeley.edu/
 
Textbook Slides: http://aima.eecs.berkeley.edu/slides-pdf/
 
Complementary Textbook: ANSI Common LISP, Paul Graham, Prentice Hall, 1995.

Tentative Schedule:

 

Week1: Introduction to AI (Chap.1) & Intelligent Agents and Problem Solving (Chapter 2) & Philosophical Foundations (Chap. 26) & AI: Present and Future (Chap. 27); AI = Rational Problem Solving {Search, Reasoning, Learning, Communication}; Bayes; LISP

 

Week2: Uninformed and Informed (Heuristic) Search (Chapters 3 4); MATLAB; Metrics and Performance Evaluation; WHITE PAPER for TERM PROJECT (due February 5)

 

Week3: Adversarial Search and Game Playing (Chap. 5)

 

Week4: Constraint Satisfaction (Chap. 6) and Planning (Chap. 10)

 

Week5: Catch Up and REVIEW for MIDTERM

 

Week6: MIDTERM (covers Chapters 1 6) (closed books and notes)

 

Week7: Knowledge and Reasoning (Chapters 7 and 8)

 

3/12: Spring Break

 

Week8: First-Order Logic: Representation and Inference (Chapters 8 and 9)

 

Week9: Biometrics and Face Recognition

 

Week10: Learning / Decision Trees and Ensemble Learning / AdaBoost (Chap. 18) and Data Mining

Week11: Uncertainty / Probability / Bayes and Inference Using Belief / Bayes Networks (Chapters 13 14)

 

Week12: Communication / Perception and Natural Language Processing / (Chapters 22 24) and Deep Learning

 

Week13: Term Project Presentations and Discussion

 

Week14: REVIEW for FINAL

 

 

Grading:

        Homework: 20%

        Term Project (due April 23): 25%

        MIDTERM: Thursday, February 26 25 %

        (cumulative) FINAL : Thursday, May 12 30 %

Honor Code

You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at http://academicintegrity.gmu.edu/honorcode/.

Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies