After successful completion of the course, students are able to describe basic concepts of machine learning (incl. data preparation, selection of suitable algorithms, evaluation) and apply them to real-world problems.

Professional and methodological competences: After positive completion of the module, students are able to

• develop a suitable strategy for dealing with a given problem (selection of algorithms and methods),
• work out and apply the basics and formal concepts of machine learning,
• develop a suitable strategy for processing real data,
• define an evaluation concept.

Cognitive and practical competences: After positive completion of the module, students are able to

• understand existing problems and their underlying concepts,
• analyse data sets and prepare them for correct use,
• apply different algorithms and solution approaches to real data,
• correctly evaluate applied methods and interpret results.

Social competences and personal competences: After positive completion of the module, students are able to independently analyse problems, apply and evaluate appropriate methods and interpret results.

Planned contents are:

• Introduction, history and taxonomy
• Basic concepts of machine learning (error bounds, data preparation and evaluation methods) and applications
• Rule-based classification and regression
• Clustering and dimensionality reduction
• Learning theory
• Kernel methods
• Probabilistic models
• Ensemble Methods
• Deep Learning
• Online, Active and Reinforcement Learning
• Outlook including fairness and ethics in machine learning

A mix of introductory online lectures (recorded and/or live), exercises with formative feedback and some live (online) sessions where the assigments are discussed.

The contents to be learned are presented in the lecture part of the course. Students will also be tasked to solve exercises related to the presented content. In addition to the exercises, students have to submit a project which they can work on individually or in groups.

The final grade will be assessed by a written examination and by the evaluation of the exercises and the project.

The following skills are recommended prior to taking the course:

• Programming skills
• Funamdental maths skills (e.g., reading and understanding formal proofs, linear algebra, statistics, probability theory)