The aim of this subject is to introduce the students to the most typical algorithms for data classification, regression and clustering - to enable students to apply machine learning techniques in practice to solve real-world problems.

The students are familiar with different algorithms from the field of machine learning. They have an overview of current machine learning methods (such as SVM, Evolutionary Algorithms, Graphical Models, Convolutional Neural Networks).

Students are able to use software libraries in the field of machine learning to perform prototype implementations and can transfer their knowledge of selected methods for implementation in an application.

Furthermore, students will learn to use logical formalisms in artificial intelligence, such as classical, intuitionistic, modal and fuzzy logics.

The students are able to specifically deal with technical literature from the field of machine learning and to transfer the presented contents to new use cases.

By the end of the module, students will be able to demonstrate the main concepts in machine learning, build and optimize (linear, non-linear) data representation models, learn to implement machine learning algorithms in software programs, apply machine learning algorithms in practical problems with different nature.