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Introduction
Fundamentals of Machine Learning
Perceptrons
Multilayer perceptrons: architecture and error backpropagation
Multilayer perceptrons: other learing techniques
Hopfield networks, simulated annealing and chaotic neural networks
Associative memory networks
Clustering I: Basic clustering models and algorithms
Clustering II: topics in clustering
Radial basis function networks
Recurrent neural networks
Principal component analysis
Nonnegative matrix factorization and compressed sensing
Independent component analysis
Discriminant analysis
Support vector machines
Other kernel methods
Reinforcement learning
Probabilistic and Bayesian networks
Combining multiple learners: data fusion and emsemble learning
Introduction of fuzzy sets and logic
Neurofuzzy systems
Neural circuits
Pattern recognition for biometrics and bioinformatics
Data mining.

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