In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.

In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.

This course will focus on inference with statistical models for image analysis. It discusses Markov random fields for image processing reasons and graphical models are for image understanding.

The fundamental elements of imperative programming languages (variables, assignments,conditional statements, loops, procedures, pointers, recursion) are explained on the basis of C++.Simple data structures (lists, trees) and fundamental algorithms (searching, sorting)are discussed and implemented. Finally, the concept of object oriented programming is briefly explained.

The course introduces fundamental concepts and algorithms of machine Learning:Bayes decision theory and maximum likelihood estimation,cross-validation, jackknife and bootstrap, hypothesis testing,classification techniques: perceptron, support vector machines,density estimation, unsupervised learning, hidden markov models,dimensionality reduction techniques.