Dr. Christos Nikolopoulos
Office: BR 197
(309) 677-2456
Email:
Class URL: and follow CS562 link for home work, notes, etc.)
CS 562 Summer 1
Intelligent Systems and Applications
Required Textbook:
Chris Nikolopoulos,Expert Systems: An Introductionto First, SecondGeneration and Hybrid Knowledge based Systems,Marcel Dekker, 1997.
Other References:
- Notes from the class website
- Video tapes
Description:
We will cover logic Programming, knowledge representation, uncertainty
and fuzzy logic. We will give an overview of Expert Systems and related
technology. We will various models of neural networks for classification, clustering, pattern recognition and Hybrid Expert Systems (a combination of neural nets or other paradigms and expert systems). Other machine learning algorithms will also be covered such as classification trees, cellular automata. Optimization using genetic algorithms will also be covered. Various software packages will be used for solving problems using expert system shells, NN packages and fuzzy logic systems.
A project will take the place of the final. Projects usually involve the usage of one of the paradigms of neural nets, fuzzy logic or genetic algorithms to solve problems such as robot navigation, data mining application on business or engineering domain data, etc.
The project code, hard copies, test runs, etc. are due on the day of the final exam.
Class delivery format:
The class is offered in an online, asynchronous format.
Questions to the instructor by email will be answered by the end of the day received.
Tests will be online. The midterm will be sent by email to each student by 9:00 a.m. of the announced day and she/he will have till 10:00 a.m. on the next day to complete, and email the answers back to the instructor. The answers could be typed in a word file or they could be handwritten and scanned. The final project, which substitutes the final exam, should be chosen by 6/22 and a written report on it will be due by July 6th,10:00 a.m. The written report on the project should be Word .doc format and written as a research paper(abstract, introduction, main sections, conclusions, references, appendices) and should be submitted by email. The homework assignments are also due on 7/6
by 10:00 a.m. by email (include code and captured screen shots of runs/outputs)
In addition to the online format, for those students who prefer the person to person interaction with the instructor, the instructor will be available at the BR156 Intelligent Systems and Robotics Lab on Wednesdays and Thursdays at 11:00 a.m. to meet with students. If you have doubts about the material and your questions were not satisfactorily answered by the video lectures or email communications, you can come if you wish to meet with the instructor.
The table below gives the reading assignments each day from the books and online sources.
# Date Topics for reading/online discussionReadings/Assignments
Day 1 / Introduction to Intelligent Systems / Chapter 1, Nikolopoulos’s bookDay 2 / Logic Programming and expert systems / Chapter 2, Nikolopoulos’s book
Day 3 / Logic Programming and expert systems / Chapter 2, Nikolopoulos’s book
Day 4 / Knowledge Representation / Chapter 4, Nikolopoulos’s book
Day 5 / Knowledge Representation / Chapter 4, Nikolopoulos’s book
Day 6 / Expert Systems implementation using EXSYS and Boolean logic / Appendix, Nikolopoulos’s book
Software package provided
Day 7 / Expert system validation, testing / Chapter 3 Nikolopoulos’s book
Day 8 / Neural Networks (perceptron, back propagation) / Chapter 6, Nikolopoulos’s book
Day 9 / Neural Networks (Hopfield, optimization with Hopfield) / Chapter 6, Nikolopoulos’s book
Day 10 / Neural Networks (Kohonen, ART, PCA and other models) / Class website notes
Day 11 / Neural Networks implementations software / Software package for implementing NNs- NeuralWare/other packages
Day 12 / Uncertainty (Bayesian, confidence factors) / Chapter 5 Nikolopoulos’s book
Day 13 / Implementing uncertainty in Expert Systems using EXSYS / Appendix on Exsys, Nikolopoulos’s book
Software package provided
Day 14 / Machine learning / View video 1, Nikolopoulos’s chapter 6, web notes
Day 15 / Machine Learning / View video 2, Nikolopoulos’s chapter 6, web notes
Day 16 / Fuzzy Logic / Chapter 5, Nikolopoulos’s book
Read fuzzy logic tutorial-link in other sources below
Day 17
Monday
6/25/2012 / MIDTERM-emailed to you by 9:00 a.m.-answers due by 10:00 a.m. next day / On chapters 1,2,3,5 and 6 Nikolopoulos’s book
and web notes on NNs
Day 18 / Fuzzy Logic implementations using FLS / Using FLS software
& View video 4
Day 19 / Data Clustering / View video 5
Day 20 / Data Clustering / View video 5, video 6
Day 21 / Genetic Algorithms / Chapter 6, Nikolopoulos’s book
Day 22 / Video on evolutionary computation / View video 3
7/4/2012 / UNIVERSITY CLOSED / Independence Day
7/5/2012 / Genetic programming, Solving optimization problems with Gas / Chapter 6, Nikolopoulos’s book
7/6/2012 / Homework, Final Project due / Homework and Final Project due by 10:00 a.m.
Assessment
200 Points Total
100 points Midterm Exam
50 points Final Project
50 pointsHomework assignments
Additional sources/videos:
Video 1 Machine Learning:
Video 2 Machine Learning:
Video 3 Evolutionary Algorithms:
Fuzzy Logic Tutorial:
Video 4 Fuzzy Logic:
Video 5 Data Clustering:
Video 6 Animation of k-nearest neighbor: