Applied Mathematics for Network and Data Sciences

Modern mathematics is at the heart of an increasing number of innovations in the field of artificial intelligence, digital communication, forensics, e-commerce, medical technology, and social networks. In particular, all kinds of machine learning applications, Industry 4.0, data sciences and web-based services are based on strong mathematical concepts in order to be efficient and secure. Modelling and development of respective algorithms and technologies rely on profound mathematical knowledge and methods. Current and previous developments in these highly innovative fields have led to a new understanding of mathematics and stimulate new mathematical research. The main characteristic of modern applied mathematics is its algorithmic view as a technology-driven field, combining challenging mathematical problems with industrial relevance and innovation. The goal of the Master's degree programme in applied mathematics at Mittweida University of Applied Sciences is to persuade young people to participate in these ongoing developments in a most competent and comprehensive way as well as to give them promising perspectives for an academic or professional career in the field of mathematics and its applications to computational intelligence, network analysis and data sciences. The focus of the programme is applied mathematics for machine learning and statistical data analysis as well as modelling. Courses regarding cryptanalysis/cryptography and graph theory for modern network and data sciences are also offered. Students attend obligatory courses on machine learning, cryptography and cryptanalysis, advanced graph theory, simulation and visualisation as well as modern analysis for statistical learning theory. Programming and presentation skills are particularly encouraged through project work. Students may also choose from a comprehensive catalogue of attractive courses from both mathematics and applications. Students participate in the various research activities in which the professors are involved. Fields of active research include machine learning, several kinds of discrete mathematics, cryptography, and stochastic processes in biomathematics. Students are free to decide on the field in which they want to write their Master's theses. A five-month internship in industry or university research enable the students to apply their acquired knowledge in practice and would ideally support and lead to the thesis topic. The thesis period takes another five months and culminates in a colloquium. For the duration of the thesis, regular attendance is not necessary. The Master's thesis is written in English (or if the student so chooses, in German). Of course, all courses are conducted in English.