The course gives an overview of many machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students should understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The student especially learns the core concepts of overfitting and underfitting and is able to find a suitable balance between these extremes in a given problem at hand.
This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks and model complexity selection and performance estimation. For pattern recognition most popular feature extraction techniques are introduced and Bayesian decision theory is studied. Both main unsupervised and supervised learning techniques are considered with emphasize on how, why and when they work.