Wir führen auf dieser Website keine Kurse auf, die eine zusätzliche Anmeldephase haben, wie die Seminare und einige der Programmierkurse. Informationen zur Anmeldung für solche Kurse werden von unserem Dekanat per Rundmail verteilt.

Diese Liste erhebt keinen Anspruch auf Vollständigkeit oder Korrektheit, im Zweifel gelten die Angaben im LSF und von den Lehrenden selbst.

Fakultät Statistik

• Advanced Statistical Learning, Prof. Groll, MD1, 9 ECTS, Tue, 16-18; Thu, 16-18
• Causality, Prof. Marx, MD E1, 9 ECTS, Thu, 14-16; Fri, 12-14
• Generalized Linear Models, Prof. Groll, MD E1, 9 ECTS, (Tue, 10-12), Wed, 12-14
• Hidden Markov and State Space Models, Dr. Michels, MD E1, 4.5 ECTS,Thu, 10-12
• Statistical Methods for Counting Processes Dr. Jakubzik, MD E1, 9 ECTS Mon, 12-14; Tue, 10-12
• Industrial Data Science II, Prof. Pauly, MD E2, 4.5 ECTS Fr, 10-12
• Simulation-based Inference, Prof. Bürkner, MD E2, 9 ECTS Tue, 14-16; Thu, 10-12
• Statistics in Toxicology I (Modelling), Prof. Rahnenführer, MD E2, 4.5 ECTS
• Econometrics, Prof. Demetrescu, MD E2, 9 ECTS Tue, 16-18; Thu 10-12
• Time Series Analysis, Jentsch, MD E2 Tue, 10-12; Wed, 8-10

Fakultät für Mathematik

• Introduction to Computational Fluid Dynamics, Prof. Kuzmin, MD E1, 5 ECTS Mo, 10-12
• Numerical Methods for PDEs, Prof. Turek, MD E1, 5 ECTS, Fr, 14-16
• Optimization, Prof. Buchheim, MD E1, 5 ECTS Thu, 10-12

Fakultät für Informatik

• Machine Learning Paradigms for Complex Data, Prof. Müller, MD E1, 8 ECTS Tue, 12-14; Thu, 14-16
• Computer Vision, Prof. Find, MD E1, 4 ECTS Tue, 14-16
• Programming Deep Learning Prof. Harmeling, MD E1, 8 ECTS Mon, 10-12; Wed, 10-12
• Theoretical Foundations of Machine Learning, Prof. Kamp, MD E1, 6 ECTS
• Verification of Neural Networks, Prof. Neider, MD E1, 6 ECTS Tue, 12-14
• Deep Reinforcement Learning, Prof. Peitz, MD E1, 8 ECTSTue, 10-12, Wed, 8-10
• Human-Centered Machine Learning, Prof. Mayer, MD E1, 6 ECTS Wed, 16-18

• Data Science for Dynamical System, 6 ECTS, Methods

   

Fakultät für Wirtschaftswissenschaften

• Game Theory, Prof. Kramm, MD E2, 7.5 ECTS Mon, 8-12
• Data and AI in Economics, Prof. Posch, MD E2, 7.5 ECTS Tue, 14-18

Statistik

• Statistical Theory, MD 2
• Data Science in Context, MD 3
• Statistical Learning for Big Data, MD 5. For MD 5, you have to take this Course
• Panel Data Analysis I, 4.5 ECTS, Methods
• Natural Language Processing, 9 ECTS, Methods or Applications, by choice
• Bootstrap Methods, 9 ECTS, Methods
• Econometric Forecasting, 9 ECTS, Methods
• Asymptotic Theory, 4.5 ECTS, Methods
• Nonparamaetric and Hihg-Dimensional Econometrics, 4.5 ECTS, Methods
• On the Theory and Practivce of Monte Carlos Simulations, 4.5 ECTS, Methods or Applications, by choice
• Applied Bayesian Data Analysis, 9 ECTS, Applications
• Industrial Data Science I, 4.5 ECTS, Applications
• Statistics in Toxicology II / Testing, 4.5 ECTS, Applications
• Survey Sampling, 4.5 ECTS, Methods

Informatik

• Cyber-Physical System Fundamentals, 8 ECTS, Methods
• Theoretical Foundations of Machine Learning, 6 ECTS, Methods
• Abstraction in Machine Learning, 6 ECTS, Methods
• Data Science for Dynamical System, 6 ECTS, Methods

Mathematik

• Programming Course: Data Science with Python, 3 ECTS, MD 3
• Introduction to Deep Learning and its Applications, 5 ECTS Methods
• Numerical Methods for PDEs, 5 ECTS, Methods