Machine Learning & Soft Computing Techniques

The Probabilistic Framework Bayesian Modeling, The Cox-Jaynes Axioms, Probabilistic Modeling and Inference Dynamic Programming, Gradient Descent, EM/GEM Algorithms, Markov Chain Monte Carlo Methods, Simulated Annealing, Evolutionary and Genetic Algorithms, Learning Algorithms

Theory- Introduction, Universal Approximation Properties, Priors and Likelihoods, Learning Algorithms: Back propagation, Application in Bioinformatics, SNNS tool.

Introduction, Prior Information and Initialization, Likelihood and Basic Algorithms, Learning Algorithms, Advantages and Limitations of HMMs. Applications in Bioinformatics, HMMer tool. Hybrid Systems: HMM & Neural Networks - Introduction to Hybrid Models, The Single-Model Case, The Multiple-Model Case, Simulation Results

Genetic Operators and Parameters, Theoretical Foundations of Genetic Algorithms, Implementation issues, Application in Bioinformatics Introduction to Fuzzy Sets, Operations on Fuzzy sets, Fuzzy Relations, Fuzzy Measures, Applications in Bioinformatics

The Learning Methodology, Linear Methods, Kernels, Feature Spaces, Generalization & Optimization Theory, Implementation, Applications in Bioinformatics, SVM lite tool.