COURSE SYLLABUS AND OUTLINE
SWENG 597C
SOFT COMPUTING
|
Dr. Jinbo Bi |
Fall II, 2008 |
|
Tuesdays & Thursdays 6-9pm |
Penn State, Great Valley |
|
(610) 448-4496 (W) |
Class Room 124 |
Soft computing uses soft computational techniques in machine learning and artificial intelligence to study and model complex phenomena where classical hard computing techniques have not yielded low-cost, analytic and complete solutions. The purpose of this course is to introduce to the students the general topics and techniques of soft computing and learning. This is a special topic course that will focus on some of the classic soft computing topics, such as fuzzy logic, neural networks and evolutionary computing, as well as some state-of-the-art soft computing and learning topics such as general classification and regression techniques, clustering techniques and dimension reduction approaches.
The course will consist of lectures, demos, and paper reviews. Lectures will serve as the vehicle to introduce new information to the students. Demos will be used to enforce the material given in lectures and students paper reviews will be used to show the state-of-the-art work from researchers in the field. Participation is encouraged during the class.
As part of the course, the students will work on a project with the goal of applying any of the studied techniques to a problem selected from a list of projects. Students are also encouraged to propose and design their own problems which need to be approved by the professor for class suitability. Teams of two students will be created for each project. Each team is required to submit a project report which includes the definition of the problem, techniques used to solve the problem and experimental results obtained. This exercise will help the team gain a better understanding of the problem and the techniques to use for it.
The outline of the course is as follows:
Day 1: Introduction to Soft Computing (review some basic linear algebra and probability)
Day 2: Fuzzy set theory (fuzzy sets)
Day 3: Fuzzy set theory (fuzzy rules and reasoning)
In-class assignment I
Day 4: Fuzzy set theory (fuzzy inference)
Round table discussion for paper review
Day 5: Classification and regression (general topics, cross validation, bootstrapping, k-nearest-neighbor)
Day 6: Classification and regression (artificial neural nets)
In-class assignment II
Day 7: Classification and regression (linear discriminant analysis, support vector machines)
Day 8: Paper review presentations
Day 9: Clustering (general topics, K-means, fuzzy K-means)
Project Round Table
Day 10: Clustering (hierarchical clustering, density-based clustering, clustering validity metric)
In-class assignment III
Day 11: Dimension reduction (principal component analysis, canonical correlation analysis, independent component analysis) )
Day 12: Genetic algorithms and evolution strategies
Day 13: Oral project presentations
Day 14: Open-book final & Project report due
Please login PSU ANGEL system for lecture slides and assignments
Possible projects (select any problems from the links below):
Tools that can help with course projects (to be complete)
Please login PSU ANGEL system for paper pdfs or links used in the mid-term paper review
"Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students. At the beginning of each course it is responsibility of the instructor to provide a statement clarifying the application of academic integrity to that course". (1989-90 Policies and Rules for Students, p.25).
DISABILITY STATEMENT:
The Pennsylvania State University encourages qualified persons with disabilities to participate in its programs and activities. If you anticipate needing any type of accommodation or have questions about the physical access provided, please contact Kathy Mingioni at 610-648-3315 in advance of your participation or visit.
SECURITY PLAN:
In the event of an emergency of any kind, you are advised to proceed to an agreed upon meeting point in a safer location - probably in the car park area. If you need special consideration in evacuating the classroom, please inform your instructor who will attempt to accommodate your special needs.
Emergency Evacuation Exercises or Actual Emergency Events:
Periodic fire/evacuation exercises are conducted in all occupied PSU Great Valley buildings. Every PSU Great Valley faculty, staff, and student is expected to exit the building and remain outside until the drill or actual event is completed. Drills are a safe opportunity to test the building emergency plan, insure that the fire alarm is working properly, and allows every employee a chance to experience the procedures.
Guidelines in the Event of a Drill or Emergency: