A | Foundations of Machine Learning


A1 | Statistical Foundations & Explainability

Research is being conducted at MCML to improve the reliability, interpretability, and acceptability of results obtained with ML algorithms for their practical application through better integration of statistical concepts. Key challenges include the integration of uncertainty quantification into ML algorithms, the explainability of ML models, the simplification of ML methods, and the incorporation of prior knowledge into ML algorithms.

LMU München

Statistical Learning & Data Science

LMU München

Biometry in Molecular Medicine

TU München

Mathematical Statistics

LMU München

Statistical Learning & Data Science

LMU München

Applied Statistics in Social Sciences, Economics and Business

LMU München

Computational Statistics & Data Science

LMU München

Functional Data Analysis

LMU München

Bayesian Imaging & Spatial Statistics


A2 | Mathematical Foundations

Some of the tremendous successes of ML have been achieved through the use of mathematical insights. The contribution of our mathematicians in MCML can be divided into two main research areas: Mathematics for ML, i.e., mathematical principles are used to develop new reliable ML algorithms, and ML mathematics, i.e. ML is used to advance mathematical research, e.g. in imaging, inverse problems, optimal control, or numerical analysis of partial differential equations.

TU München

Applied Topology and Geometry

TU München

Applied Numerical Analysis

TU München

Machine Learning

TU München

Optimization & Data Analysis

LMU München

Mathematical Foundations of Artificial Intelligence


A3 | Computational Models

Mathematical models and statistical concepts, which are core elements of ML methods, must be reflected by efficient algorithmic implementations. Furthermore, the execution of corresponding algorithms requires a suitable computational infrastructure. Currently, the steady growth of ML applications brings new algorithmic problems and computational challenges that MCML is addressing in this research area. This includes the scalability of large data sets and data streams, the adaptations of ML algorithms to modern hardware and computational infrastructures, and the exploration of methods for Constrained ML, Informed ML, Learning with Multi­relational Data such as Knowledge-Graphs, and AutoML.

TU München

Data Analytics & Machine Learning

LMU München

Artificial Intelligence & Machine Learning

TU München

Ethics in Systems Design and Machine Learning

LMU München

Database Systems & Data Mining

LMU München

Database Systems & Data Mining

LMU München

Database Systems & Data Mining