Mathematikgebäude, Hörsaal E29

Machine Learning has emerged as one of the most transformative forces in science and technology. Beneath its powerful algorithms lie mathematical foundations deeply rooted in classical disciplines such as Applied Mathematics and Systems Control. This lecture adopts a mathematician’s perspective to examine why Machine Learning works so effectively and how its data-driven paradigms can be rigorously integrated into traditional analytical frameworks.
We will revisit the historical and conceptual links between Machine Learning and Systems Control - also known as Cybernetics - a field shaped by the pioneering ideas of Ampère and Wiener. Their parallel evolution reveals a deep mathematical unity and highlights the power of mathematics to model complex systems and drive innovation.
This dual perspective is mutually enriching. On one hand, Machine Learning raises fundamental mathematical questions that challenge and inspire the mathematical community. On the other, it offers opportunities to expand the scope of classical applied mathematics by developing hybrid methodologies that integrate data-driven insights.
The lecture will conclude by outlining promising directions for future research at the intersection of Machine Learning, Applied Mathematics, and Control Theory.

Mathematisches Kolloquium: Einblicke in die Wunder und Schönheit der Mathematik

Auch im Wintersemester 2025/2026 sind einige Vorträge im Mathematischen Kolloquium vorgesehen.
Sie finden jeweils am Mittwochnachmittag statt.

Vorab findet im Seminarraum E19 um 15.00 Uhr eine Einführung in das jeweilige Thema, insbesondere auch für Studierende, statt. Ab 15.30 Uhr gibt es einen 'Institutstee', ebenfalls im Seminarraum E19.

Prof. Dr. Enrique Zuazua

Friedrich-Alexander-Universität Erlangen-Nürnberg