Description

This course introduces soft computing methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth. The principal constituents of soft computing are fuzzy logic, neural network theory, and genetic algorithms.

Program

Bachelor of Computer Science - 144 CRs

Objectives

• 1. Understand the difference between hard and soft computing methods.
  2. Be able to apply several soft computing techniques for learning from experimental data.
  3. Understand what fuzzy logic is and apply it in various applications.
  4. Apply neural networks to solve soft computing problems
  5. Use genetic algorithms to solve soft computing problems.