The latest technologies in biology, chemistry and medicine are generating huge amounts of data with previously unachievable detail, accuracy, and scope. Its analysis promises far-reaching new insights, but at the same time poses enormous new data science challenges. This core track will provide students with all the necessary theoretical understanding and practical skills to meet these challenges. State-of-the-art tools from machine learning, big data processing and Bayesian statistics will prepare participants to act as expert data scientists in a wide variety of disciplines in research and industry alike and to deliver crucial data-derived information to key decision makers.

The theoretical part of this core track covers the fundamental principles of, amongst others, data representation, statistical methods and learning theory, all with special emphasis on big data questions. These topics are, for example, covered in the course modules Fundamental of Machine Learning [IFML], Advanced Machine Learning [IAML] and Statistics and Probability Theory [MM36]. Modern methods like deep learning, probabilistic graphical models and approximate Bayesian inference are taught from the ground up to the cutting edge of current research.

The theory will enable students to approach data science problems methodologically and in a mathematically sound and systematic way. At the same time, practical training on applications in, amongst others, environmental physics, astrophysics, molecular biology, neuroscience, computational chemistry, computational cardiology, will provide the skills to efficiently address real-world problems and will help in consolidating the theoretical knowledge.

The proximity of the study program to our own machine learning research at the Faculty for Mathematics and Computer Science and at the Interdisciplinary Center for Scientific Computing will offer students early embedding into advanced research teams and tight mentorship from experts for basic research and various application areas.