Credit units

Module III Simulators For Neural Networks
15%
	Descriptors/Topics Simulators And Their Importance, Implementation Of Sensory Processing Systems, Hardware For Neural-Net Optical Character Recognition, The Role Of Time In Natural Intelligence, Hardware Concepts For Neural Networks, Rapid Prototyping For Neural Networks, Hardware For Neural-Net Optical Character Recognition
Module IV Pattern Recognition With Neural Networks	20%
Descriptors/Topics Models for Pattern Recognition, Connectionist Models, Markov Models, Boolean Feedforward Networks, Alphanumeric Character Recognition, Storing And Processing Information In Connectionist Systems, The Closed Loop Antagonistic Network (Clan), Neural Modelling Of Vision And Olfaction, Neural Computers for Vision Systems
Module V Deep Neural Networks and Motor Control With Neural Networks	20%
Descriptors/Topics Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to real life applications. Introduction to Motor Control, Spinal Network Computation, Neural Computers for Motor Control, Feedback-Error-Learning Neural Network for Supervised Motor Learning, Motor Learning by with Self- Organizing Maps, and Neural Network Application for Autonomous Mobile Robots.

Course Learning Outcomes:
Understand various form of neural network and its algorithms to solve a problem either related to prediction or classification.
Understand theory of advanced neural network algorithms and other statistical techniques.
Learn to design, implement, and evaluate a neural network-based system, process, and components.
Understand to design and develop neural network algorithms for pattern recognition.
Understand and analyze the deep learning technique for real life applications and implementation of the supervised motor learning model.