This module discusses latest trends in AI and how to integrate them in the process of designing, implementing, and maintaining IT technologies. We study large language models, recommendation systems, and reinforcement learning. Participants apply this knowledge in a project where they assist a small retailer store to integrate AI in their business.

Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.

This course provides fundamental training in areas of machine learning. The course is intended for managers and leaders who want to understand the basics of the technologies that are likely to change almost every aspect of our lives. We explain technical concepts in simple terms and no previous experience with ML is expected.

The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required.

The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required.

The course covers the basic concepts of computer programming.

This course provides the foundations of programming and working with data. Computer Science II particularly stresses code efficiency and provides the basis for understanding, design, and analysis of algorithms and data structures. In terms of working with data, foundations required for understanding experimental data and notation and basic concepts for machine learning are covered.

Computer Science II lays the foundation for understanding, designing, and analyzing algorithms and data structures.It also provides an overview of various programming concepts, such as functional programming and static and dynamically typed programming languages.

This course provides the foundations of programming and working with data. Computer Science II particularly stresses code efficiency and provides the basis for understanding, design, and analysis of algorithms and data structures.

In preparation for scientific work, especially the physics lab courses as well as semester and master's theses, students receive an introduction to many relevant aspects of data acquisition (measurement technology), software-aided data processing (error calculus, statistics, comparison with models up to machine learning) and data representation (graphs, interpretation).

This course provides a comprehensive overview of the software development process, introducing participants to essential techniques for facilitating the delivery of high-quality software products. The knowledge and practical experience gained will help managers to improve communication with software development teams, ultimately leading to higher success rates.

The course covers advanced methods of statistical learning:- Variational methods and optimization.- Deterministic annealing.- Clustering for diverse types of data.- Model validation by information theory.

This is an introduction to probability, statistics, and machine learning for students of mechanical engineering. We cover the fundamental concepts from probability theory, statistics and machine learning, with a focus on applications for mechanical engineering.