CS 230: Introduction to Intelligent Systems

CS 230: Introduction to Intelligent SystemsAnnouncements: Check here regularly for announcements!Welcome to CS 230, Spring 2014!

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)

Professor: Rebecca Bates (bates@mnsu.edu) Contact InformationCourse Hours and Location

Lectures/Lab/Discussion: M 2-3:50pmTR C 128

W 2-3:50pmTR E 315

F 2-2:50pmTR E 315

Office Hours

MondayTuesdayWednesdayThursdayFriday

11-12by appointment4-5by appointmentby appointment

Prof. Bates will also read and respond to email questions within 24 hours. You can usually expect responses to questions the same day if received prior to 10pm. If things that are useful for the entire class come up, they will be posted on the announcement section of the class webpage so check it regularly.

Course MaterialsRecommended Texts: Principles of Data Mining, 2nd. ed., Max Bramer, Springer-Verlag, 2013, ISBN 978-1-4471-4883-8 (soft cover) or 978-1-4471-4884-5(electronic) Artificial Intelligence: A Systems Approach, M. Tim Jones, Infinity Science Press, 2008, ISBN 978-0-9778582-3-1

Please purchase one of the books. Both are useful and will be used in the class so it will be a good idea to pair with a classmate so you have access to both books.

Course SyllabusStudent OutcomesStudents who complete this course will be able to:1. Define and understand basic data mining terminology.

2. Differentiate between supervised and unsupervised learning.

3. Understand basic supervised data mining methodologies used to solve problems inductively. Strategies include: decision trees, rule based, concept hierarchies, association rules, Bayesian Learning, linear regression, and neural networks.

4. Describe basic unsupervised clustering techniques for solving problems.

5. Describe how to pre-process data prior to a data mining session.

6. Use statistical and heuristic methods to report the results of a data mining session.

7. Use one or several data mining tools to perform both supervised and unsupervised learning.

8. Know and define terms basic to artificial intelligence problem solving.

9. Understand and define basic expert system terms.

10. Design a solution to a problem using a knowledge-based system.

11. Understand how certainty factors are used with knowledge-based systems.

12. Know what intelligent agents are and how they are used to solve problems.

13. Describe agent environments and their properties.

Tentative Topic List1. Intelligence (~1 week)

2. Data Preprocessing (~1 week)

3. The field of Artificial Intelligence (~1 week)

4. Agents & Environments (~1 week)

5. Statistical Approaches to Estimation and Prediction (~1.5-2 wks)

6. k-Nearest Neighbor (~1 week)

7. Decision Trees (~1 week)

8. Prediction & Classifer Performance Evaluation (~1 week)

9. Hierarchical and k-Means Clustering (~1.5 wks)

10. Uniformed & Informed search (2 weeks)

11. Fuzzy logic (1 week) Neural Networks (1 week)

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

Important DatesMidterm 1: Wednesday, February 19Midterm 2: Wednesday, April 2Final: Thursday, May 8, 2:45-4:45pm

Course Tools

Orange DataMining Software. This will be available on lab computers. You may also want to download and install it on your own computer. http://www.ailab.si/orange/ Desire2Learn: This web-based software package will be used for turning in programming assignments and feedback surveys. Additional readings will be here.

The Computer Science Lab computers host these pages. The assignments and handouts will be available here.

Other Information

CS 230 Handouts and Assignments

Additional Resources

Here is a listing of useful AI URLs (thanks to Prof. Roiger). The reference is listed below.

http://aaai.org

http://www.aclweb.org www.auai.org www.cognitivesciencesociety.org www.cra.org http://cscsi.sfu.ca www.flairs.com www.iasted.org http://cis.ieee.org http://ijcai.org www.inns.org http://isai.cs.txstate.edu http://www.sigart.org www.kdd.org www.maebashi-it.org/tcii/index.shtml www.computer.org/portal/web/tcpami www.kdnuggets.comMajority taken from: Hamilton, M., Mitchell T., and Hamilton, C. (2003). AI Matures and Flourishes in North America. IEEE Intelligent Systems, July/August, 87-89.

Page last modified by R.A. Bates on 05/08/2014 06:20 PM.