CS-802(i)-AI & NEURAL NETWORKS |
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Machine Learning & AI - Introduction, hierarchical perspective and foundations. Rote Learning, Learning by advice, Learning in
problem solving inductive learning, explanation based learning, learning from observation and discovery, learning by analogy,
introduction to formal learning theory.
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Biological neurons and brain, models of biological neurons, artificial neurons and neural networks, Early adaptive nets Hopfield nets,
back error propagation competitive learning lateral inhibition and feature maps, Stability - Plasticity and noise saturation dilemma,
ART nets, cognition and recognition. |
Neural nets as massively parallel, connectionist architecture, Application in solving problems from various are as e.g., AI, Computer
Hardware, networks, pattern recognition sensing and control etc.
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Books: |
1. P H Winston - Artificial Intelligence - Pearson Education
2. Bishop, Neural Networks for Pattern Recognition, OUP
3. Cohen, Empirical Methods for AI, PHI
4. Haykin, Neural Network, Pearson Education/PHI
5. E Charniak and W Midermott - Introduction to Artificial Intelligence - Pearson Education.
6. Hagan, Neural Network Design , Vikas
7. Shivanandan, Artificial Neural Network, Vikas
8. Bose - Neural Network Fundamentals with graphs, Algorithms and Applications - TMH.
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