**1. Introduction to Soft Computing**

- Soft computing Constituents, Characteristics of Neuro
Computing and Soft Computing, Difference between Hard
Computing and Soft Computing, Concepts of Learning and
Adaptation.

**2. Neural Networks**

**Basics of Neural Networks:**
Introduction to Neural Networks, Biological Neural
Networks, McCulloch Pitt model

**Supervised Learning algorithms:**
Perceptron (Single Layer, Multi layer), Linear separability,
Delta learning rule, Back Propagation algorithm

**Un-Supervised Learning algorithms:** Hebbian Learning,
Winner take all, Self Organizing Maps, Learning Vector
Quantization.

**3. Fuzzy Set Theory**

- Classical Sets and Fuzzy Sets, Classical Relations and Fuzzy
Relations, Properties of membership function, Fuzzy
extension principle, Fuzzy Systems- fuzzification,
defuzzification and fuzzy controllers.

**4. Hybrid system**

- Introduction to Hybrid Systems, Adaptive Neuro Fuzzy
Inference System(ANFIS).

**5. Introduction to Optimization Techniques**

Derivative based optimization - Steepest Descent, Newton
method.

Derivative free optimization- Introduction to Evolutionary
Concepts.

**6. Genetic Algorithms and its applications**

- Inheritance Operators, Cross over types, inversion and
Deletion, Mutation Operator, Bit-wise Operators,
Convergence of GA, Applications of GA