|
Sec.
|
Content
|
Hrs
|
Page
|
|
01
|
Introduction to Soft Computing :
Introduction, Fuzzy Computing, Neural Computing,
Genetic Algorithms, Associative Memory, Adaptive Resonance Theory,
Applications.
|
1-6
|
1-61
|
|
02
|
Fundamentals of Neural Network :
Introduction,
Model of Artificial Neuron, Architectures, Learning Methods, Taxonomy of NN
Systems, Single-Layer NN System, Applications.
|
7-14
|
1-40
|
|
03
|
Back Propagation Network :
Background, Back-Propagation Learning, Back-Propagation Algorithm.
|
15-20
|
1-33
|
|
04
|
Associative Memory :
Description,
Auto-associative Memory, Bi-directional Hetero-associative Memory.
|
21-24
|
1-42
|
|
05
|
Adaptive Resonance Theory :
Recap
- supervised, unsupervised, backprop algorithms; Competitive Learning;
Stability-Plasticity Dilemma (SPD), ART Networks, Iterative Clustering,
Unsupervised ART Clustering.
|
25-28
|
1-60
|
|
06
|
Fuzzy Set Theory :
Introduction,
Fuzzy set : Membership, Operations, Properties; Fuzzy Relations.
|
29-34
|
1-42
|
|
07
|
Fuzzy Systems :
Introduction,
Fuzzy Logic, Fuzzification, Fuzzy Inference, Fuzzy Rule Based System, Defuzzification
|
35-36
|
1-30
|
|
08
|
Fundamentals of Genetic Algorithms :
Introduction,
Encoding, Operators of Genetic Algorithm, Basic Genetic Algorithm.
|
37-40
|
1-50
|
|
09
|
Hybrid Systems :
Integration
of Neural Networks, Fuzzy Logic and Genetic Algorithms, GA Based Back
Propagation Networks, Fuzzy Back Propagation Networks, Fuzzy Associative
Memories, Simplified Fuzzy ARTMAP.
|
41-42
|
1-41
|
Sec.
|
Content
|
Hrs
|
Page
|
01
|
Introduction to Soft Computing :
Introduction, Fuzzy Computing, Neural Computing,
Genetic Algorithms, Associative Memory, Adaptive Resonance Theory,
Applications.
|
1-6
|
1-61
|
02
|
Fundamentals of Neural Network :
Introduction,
Model of Artificial Neuron, Architectures, Learning Methods, Taxonomy of NN
Systems, Single-Layer NN System, Applications.
|
7-14
|
1-40
|
03
|
Back Propagation Network :
Background, Back-Propagation Learning, Back-Propagation Algorithm.
|
15-20
|
1-33
|
04
|
Associative Memory :
Description,
Auto-associative Memory, Bi-directional Hetero-associative Memory.
|
21-24
|
1-42
|
05
|
Adaptive Resonance Theory :
Recap
- supervised, unsupervised, backprop algorithms; Competitive Learning;
Stability-Plasticity Dilemma (SPD), ART Networks, Iterative Clustering,
Unsupervised ART Clustering.
|
25-28
|
1-60
|
06
|
Fuzzy Set Theory :
Introduction,
Fuzzy set : Membership, Operations, Properties; Fuzzy Relations.
|
29-34
|
1-42
|
07
|
Fuzzy Systems :
Introduction,
Fuzzy Logic, Fuzzification, Fuzzy Inference, Fuzzy Rule Based System, Defuzzification
|
35-36
|
1-30
|
08
|
Fundamentals of Genetic Algorithms :
Introduction,
Encoding, Operators of Genetic Algorithm, Basic Genetic Algorithm.
|
37-40
|
1-50
|
09
|
Hybrid Systems :
Integration
of Neural Networks, Fuzzy Logic and Genetic Algorithms, GA Based Back
Propagation Networks, Fuzzy Back Propagation Networks, Fuzzy Associative
Memories, Simplified Fuzzy ARTMAP.
|
41-42
|
1-41
|
|
Sec.
|
Content
|
Hrs
|
Page
|
|
01
|
Introduction to AI
Definitions, Goals of AI, AI
Approaches, AI Techniques, Branches of AI, Applications of AI.
|
1-6
|
1-51
|
|
02
|
Problem Solving, Search and Control
Strategies
General problem solving,
Search and control strategies, Exhaustive searches, Heuristic search techniques, Constraint satisfaction problems (CSPs) and
models .
|
7-14
|
1-75
|
|
03
|
Knowledge Representations
Issues, Predicate Logic,
Rules, KR using predicate logic, KR
using rules.
|
15-22
|
1-79
|
|
04
|
Reasoning System
Over view, Symbolic reasoning, Statistical reasoning.
|
23-28
|
1-72
|
|
05
|
Game Playing
Overview, Mini-Max search procedure, Game playing
with Mini-Max, Alpha-Beta pruning.
|
29-30
|
1-36
|
|
06
|
Learning Systems
Rote learning, Learning from
example : Induction, Explanation Based Learning (EBL), Discovery, Clustering,
Analogy, Neural net and genetic learning, Reinforcement learning.
|
31-34
|
1-81
|
|
07
|
Expert Systems
Knowledge acquisition, Knowledge
base, Working memory, Inference engine,
Expert system shells, Explanation, Application of expert systems.
|
35-36
|
1-38
|
|
08
|
Fundamentals of Neural Networks
Research history, Model of
artificial neuron, Neural networks architectures, Learning methods in neural
networks, Single-layer neural network system,
Applications of neural networks.
|
37-38
|
1-38
|
|
09
|
Fundamentals of Genetic
Algorithms
Search optimization algorithm,
Evolutionary algorithm, Encoding, Operators of genetic algorithm, Basic
genetic algorithm.
|
39-40
|
1-42
|
|
10
|
Natural Language Processing
Introduction, Syntactic processing
, Semantic and Pragmatic analysis.
|
41
|
1-27
|
|
11
|
Common Sense
Introduction, Formalization of
common sense reasoning, Physical world, Common sense ontologies, Memory organization.
|
42
|
1-26
|