• About the Projekt

    The Interdisciplinary Teaching Programme on Machine Learning and Artificial Intelligence, InterKI , is a project funded within the funding initiative "Artificial Intelligence in Higher Education". It will run from January 2022 to December 2025.

    The aim of the project is to establish and test a graduated university-wide teaching programme on machine learning (ML) and artificial intelligence (AI). AI is taught as an interdisciplinary cross-sectional topic that has diverse application possibilities in basic research as well as in economy and society, but consequently also raises social, ethical and ecological challenges.

    The modular teaching program is designed to enable students to build up their AI knowledge, apply it independently and transfer it directly to various application areas. The courses take place in a broad interdisciplinary context, i.e., students from different departments take the courses together and work together on projects.

    The Center for Nonlinear Science (CeNoS) at Muenster University coordinates the implementation of the project, which involves the departments of Mathematics and Computer Science, Chemistry and Pharmacy, Sports Science and Psychology, Medicine and Physics, as well as various central institutions of the University of Münster. 

    Click here to go to the university's press release.

  • Structure of the teaching program

    Modular structure of the teaching program
    © CeNoS

    Module A: Basics

    Module A offers bachelor and master students from various disciplines with heterogeneous prior knowledge the opportunity to acquire basic ML and AI knowledge and to develop the ability to carry out first practical applications in their own area of knowledge.

    The building blocks of this module are (i) self-study courses on the mathematical prerequisites of AI, on getting started with programming and on using central software packages, (ii) user-friendly interfaces, (iii) the interdisciplinary basic lecture "Introduction to Machine Learning", (iv) an extensive library of Jupyter notebooks, and (i) one- to two-week practical courses (hackatons).

    Module B: Complex applications

    Module B builds on the basic module A and is suitable for master students and doctoral candidates.

    Here, students can acquire the competence to independently apply AI methods to current problems from the following research fields: (i) the analysis of complex dynamical systems, (ii) the theoretical analysis of molecular systems, (iii) molecular applications in the context of the development of functional molecules, (iv) the analysis of human movements, and (v) ML applications for medical imaging data.

    Complementary practical examples from industry and economy enable students to transfer knowledge to the non-university work environment

    Module C: Conceptual deepening

    The advanced Module C is aimed at master students and doctoral candidates and provides advanced knowledge required for a deeper understanding and further development of AI methods.

    Specific in-depth courses address various state-of-the-art methods, current advances in AI research and advanced concepts from the central areas of machine imaging and language processing (e.g. attention mechanisms (incl. transformers), contrastive learning strategies, capsule networks and other modern architectures).

    Theoretical in-depth courses convey the advanced theoretical concepts and delve deeper into the mathematical background. For example, connections to the mathematics of inverse problems and eigenvalue and sensitivity analysis are shown and their significance in the context of neural networks is discussed. This serves as a basis for further analyses, which include alternative and experimental training strategies as well as explainable AI methods.

    The extension of the foundations includes courses that tare not usually counted as core curriculum content of AI, but are nevertheless highly relevant to AI. These include Bayesian statistics, which is fundamental to all data-driven methods and from which basic ML concepts can be derived, as well as nonlinear dynamics, which is increasingly being incorporated into new forms of ML.

    Module D: Broadening the horizon

    Module D is aimed at bachelor and master students as well as doctoral candidates. In cooperation with various central institutions at the University of Münster, the topic of AI is examined in a broader context and current challenges are discussed.

    This includes (i) sustainability, (ii) AI-based business ideas (start-ups), (iii) the integration of the basic ideas and social aspects of AI into teacher training, as well as (iv) questions of science theory and ethics.

    Furthermore, in the course of a "Women in AI Initiative", courses of the teaching programme are explicitly addressed to (young) female researchers and specific information events are offered, e.g. in the context of the "Girls Day" (AI and Gender).