MSc (Mathematics and Computing) Programme:Artificial Neural NetworksThapar University
Price on request
Real Analysis – I
Fundamentals of Computer Science and C Programming
Discrete Mathematical Structure
Real Analysis –II
Advanced Abstract Algebra
Computer Oriented Numerical Methods
Data Based Management Systems
Computer Based Optimization Techniques
Artificial Neural Networks
Introduction: Biological Analogy, Architecture classification, Neural Models, Learning Paradigm and Rule, single unit mapping and the perception.
Concepts in ANN: Feed forward networks – Review of optimization methods, back propagation, variation on back propagation, FFANN mapping capability, Mathematical properties of FFANN’s Generalization, Bios and variance Dilemma, Radial Basis Function networks.
Recurrent Networks: Symmetric Hopfield networks and associative memory, Boltzmann machine, Adaptive Resonance Networks
Other Networks: PCA, SOM, LVQ, Hopfield Networks, Associative Memories, RBF Networks, Applications of Artificial Neural Networks to Function Approximation, Regression, Classification, Blind Source Separation, Time Series and Forecasting.
Laboratory Work: The lab work will be based on the implementations of different neural networks strategies using C/C++/LISP/PROLOG (or on MATLAB/MATHEMATICA) on various case studies.