Master Mathematics of Machine Learning and Data Science (MMLDS)
Content, Keyfacts, Specialities
Kurzbeschreibung
„The master’s program teaches the mathematical foundations of the research field “Machine Learning and Data Science” through relevant course content in pure mathematics (topology, differential geometry, dynamical systems) and applied mathematics (statistics, optimization, numerical methods, functional analysis), which are necessary for understanding and further developing these methods.
This master’s program differs from the master’s program in mathematics in its focus on the interplay of mathematical concepts and its research-oriented emphasis on machine learning and data science. Working on projects in the Data Science Lab provides training in the application of these methods and in teamwork.“
Specialities in Heidelberg
The Master’s program in Mathematics of Machine Learning and Data Science offers a mathematical perspective on current research topics in the fields of machine learning and data science. Compared to other degree programs, the lecture series and the Data Science Lab are particularly noteworthy.
Application and Preparation
Application
The master program is currently limited to 40 places. At least you need the following prerequisites
- Bachelor degree in mathematics, physics or a similar program with a (german) grade of at least 2,3 or better.
- (Completed) introductory courses worth 8 LP each in calculus and linear algebra.
- At least three (completed) introductory courses worth 8 LP each in the areas of functional analysis, differential geometry, optimization, statistics and probability theory, and numerical methods.
- English proficiency at the B2 level (with appropriate certificate )
All applicants who meet these requirements will be ranked based on their qualifications and will receive an invitation to an interview in that order. The Admissions Committee will then decide whether your application is successful.
Keep in mind that the admission requirements listed above must be met in every case; otherwise, you will have no chance of being admitted, even if fewer than 40 people apply or accept a spot in the program. It is generally also possible to apply for a different degree program, but be sure to meet the requirements for that program as well.
You can find more information on this in the admission regulations and on the website of the degree program.
Master Induction Event
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Structure of the Program
Exemplary Course of Study
he program consists of the following required courses:
- Lecture Series (Ringvorlesung)
- Data Science Lab
- Seminar on Machine Learning and Data Science
You must also take 3 of the following required elective courses (Core Modules):
- Geometric Methods for Machine Learning
- High-Dimensional Numerics
- Partial Differential Equations and Pattern Formation
- Statistical Learning and Empirical Process Theory
- Variational Methods and Numerical Optimization
In addition, you’ll take 3 specialization courses, interdisciplinary skills courses, and complete your master’s thesis with a presentation.
You can find a sample study plan in the image on the left. However, it’s perfectly fine to study for more than 4 semesters.