Artificial Intelligence and Data Science
Master of Science
Artificial Intelligence (AI) and Data Science are the driving forces behind the fast-paced advances in digitalisation and automatisation that will change our everyday life. This two-year Master's programme focuses on the theoretical foundations of Artificial Intelligence and Data Science and their applications to real life problems. Specialised lectures guide you the way to specific research areas, such as natural language processing, computer vision, and analysis of biological/medical data. Central to this programme are machine learning methods and their applications, with strong focus on deep learning.
Choosing from a range of elective courses you can tailor the programme to your personal interests. As a graduate of the Artificial Intelligence and Data Science programme, you will have a solid understanding of the mathematical and statistical foundations of AI related research. You will also have a good overview over state of the art algorithms and an in-depth understanding of how to apply these algorithms to specific research problems. In addition, you will get the skills to carry out research in academic or R&D environments and to identify how techniques of Artificial Intelligence and Data Science can provide solutions to data-based IT problems in industry.
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 2.2 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).
Advanced Programming and Algorithms
Mathematical and Statistical Foundations of Data Science Lab rotation I Lab rotation II Master Thesis Master Thesis Seminar
Methods of Artificial Intelligence in Life Science
Generative Models and Sampling Methods Algorithmic Game Theory
Relational Databases and Data Analysis
Data & Knowledge Engineering
Neuroimaging and Precision Medicine
Philosophy of Intelligence
Statistical Data Analysis
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)
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.
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
- Mathematical and Statistical foundations of Data Science
- Advanced Programming and Algorithms
- Machine Learning
- Deep Learning
- 3 elective subjects
- Lab Rotation I
- 4 elective subjects
- Lab Rotation II
- Master thesis & seminar
Programme objectives/Career prospects
Data Scientist, AI specialist
Local admission restrictions - HHU
Dr. Peter Arndt