Mathematics

If you choose this track you will take the Master Seminar in Algebra and Geometry and at least three courses among

1. Algebraic Geometry 1
2. Algebraic Number Theory
3. Algebraic Topology 1
4. Differential Geometry
5. Elliptic Curves

For a list of the elective courses have a look at the corresponding section of the Study Guide and at the MasterMath website. Here are two examples taught by our lecturers:

1. Formal Methods in Mathematics
   The focus of this course is the theory and practice of proof assistants in mathematics. The theory part will involve a treatment of the (dependent) type theory on which the underlying logical foundations of proof assistants such as Coq, Lean, and Agda are based. In the practical part, the students will learn how to use a proof assistant, for which we shall focus on Lean, together with its (main) mathematical library "mathlib".
2. Algebraic Topology 2
   This is a course with varying content. Every year different aspects of algebraic topology are treated. For instance, you might learn about the classification of fiber bundles, characteristic classes and cobordism theory.

1. Algebra and Number Theory
2. Geometry and Topology  – Center for Topology and Applications Amsterdam

1. Intercity Number Theory Seminar
2. Arbeitsgemeinschaft Differential Topology
3. Dutch Differential Topology and Geometry Seminar
4. Informal Formalization Seminar
5. Arithmetic Seminar
6. TopICS Seminar

If you choose this track you will take the compulsory courses

1. Master Seminar in Analysis and Dynamical Systems
2. Dynamical Systems
3. Functional Analysis
4. Partial Differential Equations

For a list of the elective courses have a look at the corresponding section of the Study Guide and at the MasterMath website. Here are two examples taught by our lecturers:

1. Non-linear Dynamics
   The course is meant to be a bridge from an introductory to a specialised course in dynamical systems. Our main goal is to give a taster of modern research topics in dynamical systems.
2. Mathematical Neuroscience
   The aim of this course is to explain how mathematical models of cerebral activity can be derived from first principles, and to introduce mathematical methods for their analysis, with the view of reproducing experimental data and predicting spatio-temporal cortical signals. As neuroscience models are naturally framed as dynamical systems, we will use methods from bifurcation theory, numerical analysis, pattern formation, and geometrical singular perturbation theory to analyse models at various scales.

1. Dynamical Systems – Amsterdam Center for Dynamics and Computation
2. Geometry and Topology – Center for Topology and Applications Amsterdam

1. Local Dynamics Seminar
2. Lowlands Dynamics Seminar
3. VU/Leiden/Delft Seminar

If you choose this track you will take the compulsory courses

1. Master Seminar in Stochastics (VU)
2. Asymptotics Statistics (MasterMath)
3. Measure Theoretical Probability (VU/UvA)
4. Stochastic Processes (MasterMath)

For a list of the elective courses have a look at the corresponding section of the Study Guide and at the MasterMath website. For instance, you can delve into Stochastic Processes for Finance (VU), Advanced Machine learning (VU), Statistical Models (VU), Statistics for Networks (VU), Bayesian statistics (Mastermath), Forensic Probability and Statistics (VU).

Here are two examples taught by our lecturers:

1. Statistics for High-Dimensional Data (VU)
   This course covers modern statistical and machine learning methods for high-dimensional data, focusing on controlling type I error in multiple testing and regularized estimation techniques like ridge and lasso. Theoretical foundations such as the bias-variance trade-off and Bayesian statistics are explored, along with regularization methods like shrinkage and dropout. Applications include genomics, fMRI, and economics.
2. Forensic Probability and Statistics (VU)
   The aim of this course is to understand the basics of the theory and philosophy of forensic statistics and probability, and to apply these to concrete situations. The course is a natural mixture of rigorous probabilistic and statistical theory on the one hand, and elements from biology, forensic science, legal science, and philosophy on the other.

Probability and Statistics

1. Van Dantzig Seminar
2. VU-Campus Statistics Seminar

If you choose this track you will take one among

1. Master Seminar in Analysis and Dynamics
2. Master Seminar in Stochastics

and all the three courses

1. Mathematical Biology
2. Advanced Machine Learning
3. Statistical Models

Moreover, you will invest in your knowledge of biology and medicine by following five courses at master level among those in this list. Examples include Bioinformatics, Systems Biology, Biophysics.

For a list of the elective courses have a look at the corresponding section of the Study Guide and at the MasterMath website. Here are two examples taught by our lecturers:

1. Mathematical Neuroscience
   The aim of this course is to explain how mathematical models of cerebral activity can be derived from first principles, and to introduce mathematical methods for their analysis, with the view of reproducing experimental data and predicting spatio-temporal cortical signals. As neuroscience models are naturally framed as dynamical systems, we will use methods from bifurcation theory, numerical analysis, pattern formation, and geometrical singular perturbation theory to analyse models at various scales.
2. Statistics for High-dimensional Data
   This course covers modern statistical and machine learning methods for high-dimensional data, focusing on controlling type I error in multiple testing and regularized estimation techniques like ridge and lasso. Theoretical foundations such as the bias-variance trade-off and Bayesian statistics are explored, along with regularization methods like shrinkage and dropout. Applications include genomics, fMRI, and economics.

Biomathematics

If you choose this track you will take the compulsory courses

1.Master Seminar in Stochastics
2.Mathematical Optimization
3.Advanced Machine Learning
4.Statistical Models

and two courses among the list

1.Applied Stochastic Modeling
2.Entrepreneurship in Analytics and AI
3.Optimization of Business Processes

For a list of the elective courses have a look at the corresponding section of the Study Guide and at the MasterMath website. Here are two examples taught by our lecturers:

1. Dynamic Programming and Reinforcement Learning
   The course focuses on both theoretical and practical aspects of decision-making over time in uncertain environments. Topics include full-information planning, partial-information learning, and various algorithms, from optimal to heuristic approaches. Both small-scale, intuitive problems and high-dimensional applications are covered, with practical algorithm implementation in Python.
2. Statistics for High-Dimensional Data
   This course covers modern statistical and machine learning methods for high-dimensional data, focusing on controlling type I error in multiple testing and regularized estimation techniques like ridge and lasso. Theoretical foundations such as the bias-variance trade-off and Bayesian statistics are explored, along with regularization methods like shrinkage and dropout. Applications include genomics, fMRI, and economics.

Analytics and Optimization – Amsterdam Center for Optimization, Statistics and Analytics

Analytics and Optimization seminar

Registration - You can find more detailed admission requirements and information about how to register on the programme pages of the STEM Master's programmes.

Exemptions - Did you follow the educational minor during your bachelor’s programme? Then you can apply for an exemption for 27 EC of this specialisation.

Have you obtained a second-degree teaching qualification for a STEM discipline at a University of Applied Sciences (hogeschool)? If so, you will first follow a pre-Master's programme. During the Master's specialisation, you can then apply for an exemption for 30 EC.

Visit this page for more information.