Studiengang Machine Learning [Master]

Studiengang Name Englisch:Machine Learning
Abschlussbezeichnung:Master of Science
Regelstudienzeit (Semester):4 Semester
Vorpraktikum erforderlich:nein
Zulassung Sonstiges:A Bachelor's degree (or equivalent) in either computer science, mathematics, physics or related natural sciences with a grade of at least 2.3 (German grading system). In particular, competences from the following areas are required, equivalent in content and scope to those in the BSc course in Computer Science in Tübingen: - Mathematics: one- and multidimensional analysis, linear algebra and either numerics or stochastic - Computer science: Programming, Algorithms and Data Structures. Applicants must also provide adequate proof of English proficiency. Application deadline for international (non-EU) citizens as well as German and EU citizens: 30 April
Unterrichtssprache: Englisch
Auslandssemester vorgesehen:nein
Praxissemester vorgesehen:Pflicht
Internationaler Studiengang:nein
Berufsbegleitender Studiengang:nein
Schwerpunkte:Our International Master's program in Machine Learning is about to open its doors! We will start accepting applications in the very near future, and the first courses will be taught in winter term 2019/2020. Study content The international Master’s program in machine learning offers a wide choice of courses in computer science and, apart from a few mandatory courses, allows students to choose their subjects according to interest. Students will attend lectures, seminars, and project lab courses under the supervision of scientists who introduce them to basic and applied research and current topics in machine learning. To pick up on scientific trends and make the best use of the current state of research, the curriculum relies heavily on the strong research presence on site, in machine learning as well as the wider field of computer science: top-level researchers in all major methodological branches of machine learning are present in Tübingen – personnel that will actively engage in teaching for the Master’s Program Machine Learning. Since the field is obviously very young and currently developing extremely rapidly, training will naturally be based on the most recent insights and the most pressing research questions of these teaching researchers. Project work and the Master’s thesis will offer students the opportunity to develop code for research purposes and their own scientific projects. As interdisciplinarity is an important aspect, the Master’s thesis can be supervised by a professor any subfield of computer science.
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Universität Tübingen

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