CSC 8520, Fall 2005
Artificial Intelligence
Th 6:15 - 9:00, Mendel G43
Paula Matuszek

Syllabus:

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Aug 25: General introduction and discussion.
Sep 1: Introduction to AI.    Introduction to Prolog.     Reading: Chapter 1
Intro presentation.     Assignment 1.
Prolog presentation.    Additional Prolog Resources.     Prolog Assignment 1.
Note: Additional versions of slides for the book are available at the text book web page.
Sep 8: Intelligent Agents, Problem Solving Agents. Reading: Chapters 2, 3.1-3.3
Agents presentation.     Assignment 2.
Sep 15: Search. Reading: Chapters 3-6
Search presentation.    Prolog 2 presentation.
Prolog Sample World.     Prolog Sample World 2.
Greedy Best First Prolog Program.
Prolog Assignment 2 .
Sep 22: Cancelled
Sep 29: Search Continued. Reading: chapter 6
Search Continued Presentation     Assignment 3 .
Oct 6: Knowledge Representation. Reading: Chapter 10
Knowledge Representation presentation.
Oct 13: Fall Break
Oct 20: Logic. Reading: chapters 7-9
Logic Presentation     Resolution Overview.     Monty Python Does Resolution.
Oct 27: Knowledge Representation continued.
Knowledge Representation Presentation, continued.     Project Information
Nov 3: Uncertainty. Reading: Chapters 13 and 14
Uncertainty presentation.
Nov 10: Knowledge and Observation-based Learning. Readings: Chapter 18.
Machine Learning presentation.
Prolog Assignment 3.
Sample world for assignment.
Sample ontology from lab.
Project Evaluation comments.
Nov 17: Student Presentations and Conclusions
Conclusions presentation
Neural Nets, Brian Talecki.    Description.
Ethical Issues in AI, Nikki Lamontagne.    Description.
Philosophical Foundations of AI, Tim White.    Description.
Nov 24: Thanksgiving Break
Dec 1: Student Presentations.
Computer Vision, Eric Clark.    Description.
Visual Perception, Eric Robinson .    Description.
Image Recognition, Ducson Nguyen.    Description.
Thejonatha Annareddy.    Description.
Statistical Learning from Decision Trees. Vamsi Dhannapaneni.    Description.
Dec 8: Student Presentations.
Neural nets and data mining, Luis Ahumada.    Description.
Statistical Machine Learning, Brian Wagner .    Description.
Genetic Algorithms, Abdo Achkar.    Description.
Autonomic Computing, Joseph Behnken.    Description.
Kieth Simons.    Description.
Dec 15: Final Exam.