CS 230: Introduction to Intelligent Systems
Fall 2008
Syllabus

Course description: This course covers fundamental material on data mining and knowledge discovery. Several data mining methods including decision tree algorithms, association rule generators, neural networks, and Web-based mining are detailed. Rule-based systems and intelligent agents are introduced as methods for building decision models. Students learn how to use intelligent tools to help solve real-world problems.

Pre-requisites: CS 110 (Computer Science I)
 

Course Website
http://bates.cs.mnsu.edu/cs230
Check the website regularly for announcements and updates. 

Course Hours and Location

Lectures: TTh 11-11:50am	WH 286
Lab: W 11-11:50am	TR C128
Lab: F 11-12:50pm	WH 119

 
Office Hours

Monday	Tuesday	Wednesday	Thursday	Friday
by appointment	2-3:30	2-3:30*	2-3:30	1-3

*in WH 283
If things that are useful for the entire class come up, they will be posted on the announcement section of the class webpage so please check it regularly.

Course Materials
Recommended Texts:

    Principles of Data Mining, Max Bramer, Springer-Verlag, 2007, ISBN 978-1-84628-765-7
    Artificial Intelligence: A Systems Approach, M. Tim Jones, Infinity Science Press, 2008, ISBN 978-0-9778582-3-1

Student Outcomes

Students who complete this course will be able to:

1. Define and understand basic data mining terminology.
2. Differentiate between supervised and unsupervised learning.
3. List and define the steps of the knowledge discovery in databases (KDD) process.
4. Understand basic supervised data mining methodologies used to solve problems inductively. Strategies include: decision trees, rule bases, concept hierarchies, association rules, Bayesian Learning, linear regression, and neural networks.
5. Describe basic unsupervised clustering techniques for solving problems.
6. Describe how to pre-process data prior to a data mining session.
7. Use statistical and heuristic methods to report the results of a data mining session.
8. Use one or several data mining tools to perform both supervised and unsupervised learning.
9. Know and define terms basic to artificial intelligence problem solving.
10. Understand and define basic expert system terms.
11. Design a solution to a problem using a knowledge-based system.
12. Understand how certainty factors are used with knowledge-based systems.
13. Know what intelligent agents are and how they are used to solve problems.

Tentative Topic List

1. Data Preprocessing (~1.5-2 wks)
2. Exploratory Data Analysis (~1.5-2 wks)
3. Statistical Approaches to Estimation and Prediction (~1.5-2 wks)
4. k-Nearest Neighbor (~1.5-2 wks)
5. Decision Trees (~1.5-2 wks)
6. Hierarchical and k-Means Clustering (~1.5-2 wks)
7. Association rules (~1.5-2 wks)
8. Model Evaluation Techniques (~1.5-2 wks)

Grading
Homework and Lab work: 45%
2 Midterm Exams: 35%        
Final exam: 20%

Important Dates
Midterm 1: Wednesday, October 1
Midterm 2: Wednesday, November 5
Final: Wednesday, December 10, 10:15-12:15pm

Course Tools

• HPC & Krypton: You will get an account on both of these machines.  If you haven't set up a krypton account, please do so since that will be the portal to the HPC (High Performance Computer).  You can set up the account through the mavmail/mavdisk account and password pages.
• Desire2Learn: This web-based software package will be used for turning in programming assignments and feedback surveys.
• Password protected webpages: The Computer Science Lab computers host these pages.  The assignments and solutions will be available on the web but will be password protected.
• Supplemental materials and information will be made available on the course website.