Principals of soft computing for computer science

Unknown April 01, 2018
Soft computing refers to intelligence of the computers.In this course you will learn on what principles computers are working.Have you ever notice, why and how computers work so fast, why there is no deadlocks, who it manages  all its resources.If you do not know anything you need not to be worry.In this course you will study principles of soft computing.But keep in mind not hard computing.Artificial course must be covered in order to understand this course.

Things you will cover in this  course:

  • Introduction to Soft Computing: Soft-Computing, Intelligent Systems and Soft
  • Computing, Importance, Decision Support Systems, Soft Computing for
  • Smart Machine Design. Fuzzy Set Theory: Fuzzy Systems, Fuzzy Sets,
  • Fuzzy Logic, Fuzzy Rules/Relations, Membership Functions, Fuzzification
  • and Defuzzification, Fuzzy System Design, Fuzzy Arithmetic, Decision
  • Making With Fuzzy Information, Fuzzy Classification and Clustering. Neural
  • Networks: Single-Layer Networks, The Multi-Layer Perceptron, Radial Basis
  • Functions, Error Functions, Parameter Optimization Algorithms, Learning and
  • Generalization, Bayesian Nets: Symmetric Matrices, Dynamic Neural
  • Networks and their Applications, Neuro-Fuzzy Systems. Evolutionary
  • Computation and Genetic Fuzzy Systems: Introduction GA For Problem
  • Solving, Theoretical Foundations. Machine Learning: Concept Learning and
  • the General-to-Specific Ordering, Decision Tree Learning, Evaluating
  • Hypotheses, Computational Learning Theory, Instance-Based Learning,
  • Learning Sets of Rules, Analytical Learning, 
  • Combining Inductive and
  • Analytical Learning. Programming with Matlab.

Reference material:

1. Soft Computing and Intelligent Systems Design: Theory, Tools, and Applications by F. Karray, C. De Silva, Addison-Wesley; 1st Edition (June 4, 2004). ISBN-10: 0321116178
2. Fuzzy Logic with Engineering Applications by T. Ross, 3rd Edition, Wiley; 3rd Edition (March 1, 2010). ISBN-10:047074376X
3. Neural Networks and Pattern Recognition by C. Bishop, Oxford University Press, 1996. ISBN-10: 0198538642
4. An Introduction to Genetic Algorithms by M. Mitchell. A Bradford Book;Third Printing Edition (February 6, 1998). ISBN-10: 0262631857
5. Machine Learning by T. Mitchell, McGraw-Hill Science/Engineering/Math;1st Edition (March 1, 1997). ISBN-10: 0070428077.

Other Information:

Course code:  NA

Prerequisites:  Intro. to Artificial Intelligence

Credit Hours:  3

Lectures: 3

Labs:      0  

      
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