Syllabus For The Subject ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK

UNIT - 01

INTRODUCTION - PROBLEMS - PROBLEM SPACES AND SEARCH - HEURISTIC SEARCH

TECHNIQUES - KNOWLEDGE REPRESENTATION ISSUES - REPRESENTING KNOWLEDGE

USING RULES - SYMBOLIC REASONING UNDER UNCERTAINTY

UNIT - 02

STATISTICAL REASONING - WEAK AND STRONG SLOT - FILLER STRUCTURES - GAME

PLAYING - PLANNING

UNIT - 03

UNDERSTANDING - NATURAL LANGUAGE PROCESSING - PARALLEL AND DISTRIBUTED

AL

UNIT - 04

INTRODUCTION - BACKGROUND - KNOWLEDGE BASED INFORMATION PROCESSING

UNIT - 05

NEURAL - NEURAL INFORMATION PROCESSING - HYBRID INTELLIGENCE

UNIT - 06

BASIC NEURON MODEL - NETWORK PROPERTIES - NODE PROPERTIES - SYSTEM

DYNAMICS - INFERENCE AND LEARNING

UNIT - 07

CLASSIFICATION MODEL - ASSOCIATION MODEL - OPTIMIZATION MODEL - SELF

ORGANIZING MODELS

UNIT - 08

LEARNING - DEFINITION - SUPERVISED & UNSUPERVISED LEARNING - STATISTICAL

LEARNING - NEURAL NETWORK LEARNING

UNIT - 09

BACK PROPAGATION - GENERALIZATION - RADIAL BASIS FUNCTION - REINFORCEMENT

LEARNING - TEMPORAL DIFFERENCE - ART

UNIT - 10

GENETIC ALGORITHMS - COMPLEX DOMAINS - EXPERT SYSTEMS HEURISTICS -

HIERARCHICAL MODEL - HYBRID MODEL - DIFFERENTIATION MODEL - CONTROL

NETWORKS

UNIT - 11

KNOWLEDGE BASED NEURAL NETWORKS - RULE BASED NEURAL NETWORKS -

NETWORK TRAINING - NETWORK REVISION - EXAMPLES OF THEORY REVISION -

DECISION TREE BASED NEURAL NETWORKS - CONSTRAINED BASED NEURAL

NETWORKS - INCREMENTAL LEARNING

UNIT - 12

Vinayaka Missions University,Directorate of Distance Education

Salem India

MASTER OF SCIENCE IN SOFTWARE ENGINEERING

1 Yr.

ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK (MSCSWE)(

2050156)

FUNDAMENTAL PRINCIPLE - NEURAL NETWORK APPROACHES - PROBABILISTIC NEURAL

NETWORKS - POLYNOMIAL ADALINES - CASCADE CORRELATION LEARNING -

INCREMENTAL RBCN