Artificial Intelligence and Data Science
Master of Science

The course has restricted admission. A prerequisite for starting studies is a first university degree, with the required necessary background education as determined in the exam regulations.

In brief, you need a Bachelor's degree in either Mathematics, Computer Science, Physics, Electrical Engineering or a related area, whose curricula include the modules Analysis I + II, Linear Algebra I, and Stochastics I that together comprise 30 ECTS and were taught at a level that corresponds to the level for computer scientists at HHU (see the FAQ for details). The examination board determines whether you fulfil the entrance requirements.

 The final grade of your bachelor's degree must be between 1.0 and  2.5 with respect to the German grading system. You can convert grades to the German system by using the 'Bavarian Formula'.

 For a degree course that is mainly in English, student applicants who have not obtained their academic qualification at an 'English-language institution', or not hold a Bachelor Degree/Masters Degree that was completely taught in English, or who are not English native speakers must prove sufficient fluency in English. The following language certificates are accepted:

 a) Test of English as Foreign Language (TOEFL), Paper-based (min 500 points), Computer-based (min 200 points), or Internet-based Test (IBT, min 80 points),

b) IELTS test with a score of at least 6.0.

c) Cambridge B2 First Certificate, formerly known as Cambridge English: First (FCE)

d) German Abitur certificate, showing that English has been constantly taken as a subject and passed with the grade of "sufficient" up to the end of the qualification level 1 (grade 11 at G8-Abitur, otherwise grade 12).

Obligatory subjects:

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Advanced Programming and Algorithms

Deep Learning

Machine Learning

Mathematical and Statistical Foundations of Data Science Lab rotation I Lab rotation II Master Thesis Master Thesis Seminar

Elective subjects:

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Methods of Artificial Intelligence in Life Science

Generative Models and Sampling Methods    Algorithmic Game Theory

Causality

Computational Linguistics

Relational Databases and Data Analysis

Data & Knowledge Engineering

Markov Chains

Neuroimaging and Precision Medicine

Philosophy of Intelligence

Statistical Data Analysis

Statistical Learning

Introduction to Logic Programming

Master Seminar Advances in Data Science Natural Language Processing Numerical Methods for Data Science Introduction to Linear Optimization Reinforcement Learning Spoken Dialogue Systems Topological Data Analysis Information Theory Spectral Graph Theory and Graph Signal Processing Seminar Advanced Mathematical and Numerical Methods in Data Science (5 CP)

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The program includes two Lab Rotations (6 weeks each) where students carry out practical work at Research Groups inside or outside HHU or in R&D environments at companies. The 6 weeks workload (10 ECTS) for a Lab Rotation need not to be taken in one block. The time a student need to be physically present at the Lab can be negotiated with the supervisor. Lab Rotations are offered by all lecturers that give courses within the Master s Program and selected groups at the Jülich Research Center (e.g. the Jülich Supercomputing Centre). Students must get in contact with Research Groups or R&D departments well ahead before the Lab Rotation starts. The supervisors of the Lab Rotation select among applicants based on their suitability for the project.

 Students can apply to any research group that offers data science projects for a Master Thesis (  6 Months).

Ideally, the thesis should be carried out in one of the two groups where a Lab Rotations has been completed. Students are co-supervised by a member of the chosen research group and a lecturer of the Master's Programme.

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It is recommended that students choose courses that are related with respect to their content. Possible focus areas listed below.

A specialisation at the early stage of a Master s can be of advantage when applying for jobs, Grad Schools, and PhD programs that have similar focus.

Possible focus areas:

1) Natural Language Processing

2) AI Methods

3) Life Sciences

4) Mathematics & Statistics

Semester 1:

- Mathematical and Statistical foundations of Data Science

- Advanced Programming and Algorithms

- Machine Learning

Semester 2:

- Deep Learning

- 3 elective subjects

- Lab Rotation I

Semester 3:

- 4 elective subjects

- Lab Rotation II

Semester 4:

- Master thesis & seminar

See also https://www.heicad.hhu.de/fileadmin/redaktion/HeiCad/heicad_pictures/heicad_master/StudyProgram.png

Data Scientist, AI specialist