Pattern Recognition

Pattern recognition systems. Bayesian classifiers, k-nearest neighbor. Parametric estimation of probability density function (maximum Likelihood estimation, maximum a posteriori). Non parametric estimation of probability density function (Parzen windows). Linear classifiers, non linear classifiers. Perceptron algorithm. Multilayer neural networks. Feature generation: contour representation and contour tracing, chain code, polygon, signatures, linear transforms, Fourier Transform, regional features, image recognition, bias and variance, texture.

The objectives of the course are to familiarize students with:

• Basic biochemical concepts
• The structure, function and chemical properties of biomolecules (proteins, carbohydrates, lipids and nucleic acids)
• Enzyme kinetics
• Coupled reactions and enzyme-catalyzed reactions of the human body that lead to the essential for survival production of biomolecules, energy and membrane potential
• The regulation of key metabolic pathways, and how these reflect the energy balance necessary to keep the state of health in man.

Αναγνώριση Προτύπων , Theodoridis S., BROKEN HILL PUBLISHERS LTD, 1η έκδ./2011, ΑΘΗΝΑ, 13256974

Written examination at the end of the semester and optional tasks.