Pattern Recognition and Machine Learning

The basic formulations of the statistical decision problem are presented. The necessary knowledge about the (statistical) relationship between observations and classes of objects is acquired by learning on the raining set. The course covers both well-established and advanced classifier learning methods, as Perceptron, AdaBoost, Support Vector Machines, and Neural Nets.

This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.

To teach the student to formalize statistical decision making problems, to use machine learning techniques and to solve pattern recognition problems with the most popular classifiers (SVM, AdaBoost, neural net, nearest neighbour).

Knowledge of linear algebra, mathematical analysis and probability and statistics.

• 1.Duda, Hart, Stork: Pattern Classification, 2001.
• 2.Bishop: Pattern Recognition and Machine Learning, 2006.
• 3.Schlesinger, Hlavac: Ten Lectures on Statistical and Structural Pattern Recognition, 2002.

Lectures, Practises, Self-study, Exercises, Tutorial sessions