CSCI 6962   -   Soft Computing   -   Fall 2001

Tuesday 6:00-9:00pm, Ricketts 212
 
 

Instructors: Bill Cheetham, Kai Goebel 

 

Office: Ricketts 212

 

Office Hours: TBD 

 

Phone: 387-5222 (Bill),  387-4194 (Kai)

 

Email: cheetham@cs.rpi.edu, goebel@cs.rpi.edu

 

Calendar

The viewgraphs are in pdf format. You can use ghostview or adobe acrobat reader to view the pdf.
 

Note: viewgraphs are linked for past and current lectures/homework only
 
 

DATE 

TOPIC 

READING 

OUT/DUE 

8/28

Course Description - Kai

Introduction: What is SC? Why is it useful? - Bill,
Matlab tutorial(here also as postscript) - Kai 

Fuzzy Sets; - Kai

(slides in 6 slide/page format: CD, Intro, Matlab, FS,)

Jang: Forward, Preface
Jang: 1

HW 1, data out

9/4

Fuzzy Reasoning; Fuzzy Inference - Kai

(6slide/page format: FR, FI)

Jang: 2, 3, 4

HW 2 out
Due: HW 1

9/11

Class Cancelled 

 



9/18 

Gradient descent optimization: least squares methods - Bill 

Genetic Algorithms -Bill
in-class exercise: class acts as GA - Bill

Applications of GA's - Class

(6slides/page format: Gradient, GA, CryptoQuote)

Jang 5, 7.2

 

Due: HW 2

HW 3 (with iris2D.dat), out

9/25 

Fuzzy Applications - Kai
Applications of Fuzzy Logic  - Class

(6slides/page format: DT, FC, FA, FIP, FDF, FD, IF) 

Jang: 14, 15, 16

Lecture notes

HW 4, out

Due: HW 3

10/2 

  Fuzzy Image Processing; Fuzzy Data Fusion; Fuzzy Diagnosis; Neural Networks: Supervised Learning: Hopfield Nets, Perceptrons, gradient descent, multilayer nets, backpropagation, overfitting - Kai
Applications of Neural Networks – Class

(6 slides/page format: NN). 

Jang: 9 

HW 5 out;
Due: HW 4

10/9 

NO CLASS - 



 

10/16

Supervised Learning summary; Reinforcement Learning, Unsupervised Learning; Clustering & Classification - Kai

Applications of Neural Networks – Class

Jang: 9, 10, 11, 15

HW 6 out (iris4D.dat)
Due: HW 5

10/23 

Case-Based Reasoning: nearest neighbor, explanation-based learning, case selection, case adaptation -Bill
Applications of CBR: (Watson) - Bill

Automated Collaborative Filtering - Bill
(6 slides/page format:  CBR, CBR_Apps)

Watson: 1, 2, 3, 4.1

HW 7 out
Due: HW 6

10/30

Applications of CBR: Color Matching - Bill 
in-class exercise: determining a salary for baseball players -Bill 
(6 slides/page format: Color

Watson: 5,6,7,8

HW 8 out

(appraiser.m, house_database.dat,

test_houses.dat)
Due: HW 7

Including Project Plan

11/6

Applications of CBR: APVT, C4.5 - Bill
Applications of CBR - Class

CBR for Help Desks, ELSI – Bill
(6 slides/page format: APVT, Help Desks

 

HW 9 out

Due: HW 8

11/13 

Hybrid Systems: ANFIS, Fuzzy Filtered NN & Neural Fuzzy Systems, GA tuned Fuzzy System, Adaptive Fuzzy Clustering, etc., Summary Remarks - Kai
(6 slides/page format: ANFIS, NN, HS, SR ).

Handouts 

HW 10 out
Due: HW 9

11/20 

BBN - demo - Bill
Determining when to use a technique - Bill
Knowledge Management - Bill 

(6 slides/page format: BBN

 

HW 11 out
Due: HW 10

11/27

one-on-one project discussions

 

Due: HW 11
Due: Project Update

12/4

Presentations & Pizza

 

Due: Final Project

Policies


August 27, 2001
goebel@cs.rpi.edu; cheetham@cs.rpi.edu