course descriptions

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first year

While the built-up of the programme is guided by certain rules, the programme offers ample opportunity to take electives. Your choice of electives will partly depend on which specialisation you choose. Therefore, the educational profile of the programme is characterised on the one hand by the three specialisations within the programme and on the other hand by the attention paid to mathematical modelling.

Second year

For the specialisations SACS, MOR and MDS, your second year starts with an internship, a period of practical training that takes place outside the university. The internship has a workload of 20 EC and has opportunities to be carried out abroad. The remaining 40 EC of the second year are devoted to your Graduation Project, which includes a literature review.

For the specialisation AI4Health, you take a few more courses, possibly together with a couple of case studies and then finally, execute a 30 EC Graduation Project at a health-related institution outside the university.

During the execution of your graduation project you are given the greatest opportunity to demonstrate that you have acquired the qualities outlined in the final qualifications of the programme on a research topic related to your chosen specialisation.


Courses

Core (17 credits)

Obligatory courses for all Applied Mathematics students: 

Mathematical Systems Theory, Applied Analysis and Computational Science

Extra core courses for the specialisation in Mathematical Systems Theory, Applied Analysis and Computational Science:

Operations Research

Extra core courses for the specialisation in Operations Research:

Mandatory: Three out of eight courses

Electives

Discrete Optimisation - 6 credits

Complex Networks - 5 credits

Measure & Probability - 5 credits

Limits to Computing - 5 credits

Mixed-Integer Optimisation - 5 credits

Information Theory and Statistics - 5 credits

Game Theory - 5 credits

Scheduling - 6 credits

Markov Decision Theory - 5 credits

Spatial Statistics - 5 credits

Stochastic Processes - 5 credits

Applied Statistics - 6 credits

Queueing Theory - 6 credits

Capita Selecta OR - 5 credits

Applied Queueing Models - 5 credits


Mathematics of Data Science

Extra core courses for the specialisation in Mathematics of Data Science: 

Mandatory, three out of six

Advanced courses: at least three courses

Deep Learning – From Theory to Practice - 5 credits

Discrete Optimisation - 5 credits

Complex Networks - 5 credits

Markov Decision Theory and Algorithmic Methods - 5 credits

Machine Learning 1 - 5 credits

Data Science - 5 credits

Statistical Learning - 5 credits

Applied Statistical Learning - 5 credits

Information Theory and Statistics - 5 credits

Capita Selecta Statistics - 5 credits

Spatial Statistics - 5 credits


Artificial Intelligence for Health

Extra core courses for the specialisation in Artificial Intelligence for Health: 

Mandatory, added by two out of five marked **

Electives

Machine Learning 1 - 5 credits

Applied Statistical Learning - 5 credits

Deep Learning – From Theory to Practice - 5 credits

Complex Networks - 5 credits

Electives (28 credits) 

Selection of 28 credits according to your preference and within the framework of the chair.

Course programmes are provided within specialisations and can be found here. 

Mastermath

The Departments of Mathematics of the Dutch universities have combined their efforts to enhance their Master's in mathematics. Part of the cooperation is aimed at organising joint courses in mathematics. These courses offer you the highest quality of instruction and open up opportunities for interaction with students of other institutes of mathematics. In our master's you will take two or more courses that are offered by Mastermath.

More about Mastermath on the website. 

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