LFU:online

Prerequisites for registration: none

Learning Outcome: Having successfully completed this module, students are able to actively participate in the discussion of the current state of knowledge in the area of the dissertation topic and can critically reflect on and discuss issues with experts in the chosen special discipline of chemistry.

Prerequisites for registration: successful completion of compulsory module 1

Learning Outcome: Students develop their own research results, present an Auditorium and develop them further in scientific discourse.

Prerequisites for registration: none

Learning Outcome: The students have knowledge needed for the completion of the dissertation.

Prerequisites for registration: none

Learning Outcome: Students of this module understand the content of the lectures and they can reproduce and apply it. They have acquired the ability to produce similar content by themselves. They can identify optimisation problems in applications and transfer them into a mathematical formula and implement the corresponding algorithms and solution procedures appropriately. Moreover, they should have gained a fundamental understanding of the methods of mathematical optimisation.

Prerequisites for registration: none

Learning Outcome: Students completing this module can deal methodologically correctly with an area of mathematics and present the results in both written and oral forms that are easy to understand.

Prerequisites for registration: none

Learning Outcome: Students who have completed that module have acquired a deepened knowledge of a branch of higher mathematics by autonomous studies. Moreover they are familiar with relevant mathematical literature and can judge its mathematical content. They are able to examine problems of higher mathematics in a creative and methodically correct manner and to present the result of those examinations in written form and orally as to be understood well by experts. The contents of the seminars are oriented on current research topics.

Prerequisites for registration: none

Learning Outcome: Having successfully completed this module, students are able to discuss the current state of knowledge in the area of the dissertation and can critically reflect on and discuss issues with experts of the chosen sub discipline. On this basis, they are able to make their own contributions to research. Likewise the students have interface knowledge at a high professional level which is needed for the implementation of the dissertation.

Prerequisites for registration: none

Learning Outcome: Having successfully completed this module, students are able to discuss the current state of knowledge in the area of the dissertation and can critically reflect on and discuss issues with experts of the chosen sub discipline. On this basis, they are able to make their own contributions to research. Likewise the students have interface knowledge at a high professional level which is needed for the implementation of the dissertation.

Prerequisites for registration: none

Learning Outcome: Having successfully completed this module, students are able to actively participate in the discussion of the current state of knowledge in the area of the dissertation topic and can critically reflect on and discuss issues with experts of the chosen partial discipline. On this basis, they are able to make their own contributions to research.Likewise the students have interface knowledge at a high professional level which is needed for the implementation of the dissertation.

Prerequisites for registration: absolvierte Lehrveranstaltungen im Umfang von mindestens 15 ECTS-AP aus Pflichtmodul 1

Learning Outcome: Studierende erwerben tiefgehende Kenntnisse über Supervised Learning mittels Verteilungsregression, über Unsupervised Learning für multivariate Daten sowie Supervised Learning mittels algorithmischer Modelle. Sie besitzen die Fähigkeit, für eine konkrete Aufgabe eigenständig ein wahrscheinlichkeitstheoretisches Modell auszuwählen und anzupassen, insbesondere fällt darunter die Wahl geeigneter Antwortverteilung, Regressoren und Algorithmen zur Parameterschätzung. Sie sind in der Lage, dimensionsreduzierende Verfahren sowie Scaling, Clustering und Assoziationsanalyse anzuwenden. Für eine konkrete Problemstellung können sie eigenständig ein prädiktives Modell auswählen und anpassen, insbesondere setzen sie flexible Lernstrategien unter Verwendung entsprechender Bausteine (Base Learners, Kernels, Regeln usw.) sowie Hyperparameter-Tuning um.

Prerequisites for registration: successful completion of compulsory module 1

Learning Outcome: Students are acquainted with the practice of scientific writing as well as the publication and review process. They are able to present their research results in plenary and to subject the results to a critical evaluation.