Principles of Soft Computing

INTRODUCTION:

Soft Computing refers to a collection of computational techniques in computer science, artificial intelligence and engineering disciplines which attempt to study, model and analyze complex problems -those for which more conventional methods have not yielded low cost, analytic and complete solutions. Unlike conventional computing, soft computing techniques are tolerant of imprecision, uncertainty and approximations. This course introduces students with soft computing techniques.

COURSE CODE: CS-4863

CREDIT HOURS: 3

PREREQUISITES: CS-3143 (Design and Analysis of Algorithms)

COURSE LEARNING OUTCOMES: At the end of this course students will be able to:

Differenciate between certain and uncertain problems and approximation algorithms or heristic to solve those problems.
Analyze uncertainity of a problem on the basis of factor taught.
Describe the general understanding of subject with a multi-dimensional approach.

TEXT BOOK:

Soft Computing and Intelligent Systems Design: Theory, Tools, and Applications: by F. Karray, C. De Silva, Addison-Wesley; 1stEdition (June 4, 2004). ISBN-10: 0321116178

COURSE CONTENTS:

Introduction to Soft Computing

Soft-Computing: Introduction to Intelligent Systems and Soft Computing

Pillars of Soft Computing

Machine Learning Introduction: Supervised and Un-supervised

Classification Algorithms: Decision Tree and Naive Baye’s. experimentation on weka.

Linear Regression: Uni-Variate Linear Regression and Multi-variate Linear regression.

Clustering: k-Mean Clustering, K-medoid Clustering and Variants. Also experimentation on weka.

Neural Network: Artificial Neuron, multi-Layer Perceptron, Back propagation, Generalizing a Neural Network, Radial Based Neural Network.

Kohonen SOM, Learning Vector Quantization.

Introduction to Fuzzy Logic, Fuzzy Rules/Relations, Membership Functions, Defuzzification.

Evolutionary Computation: Hill Climbing Search, Genetic Algorithm, Simulated Annealing. Swarm Intelligence: Ant Colony Optimization, Particle Swarm Optimization, Blind Naked Mole Rat Optimization.

Concept Learning and the General-To-Specific Ordering.

COURSE ASSESMENT: