Course information

Bayesian statistical theory for pattern recognition, Decision functions, Categorization with decision functions, Bayes classifiers with training, Neyman-Pearson classifier, Learning algorithms, Cluster finding, ManMin algorithms, K-means, Non-Parametric Decision Theory - connection to optimization theory, Unsupervised learning, Dimensionality reduction - Fisher technique, divergence criterion, entropy, Karunen-Loeve, Introduction to the basic concepts of neural networks, Perceptron, Basic architectures: Feedforward networks, Feedback networks, Pattern recognition with Neural systems.