Computer Science

The prime purpose of the Advanced GIS course is to widen your knowledge and skills in the practical – foremost analytical – application of Geographical Information Systems using raster models and spatial network models. As a consequence, a substantial part of the course consists of practical assignments in which participants train their skills in GIS analysis. At the same time, start your own analytical GIS project on planning a new road in Friesland. The practical assignments are partly integrated with the project. Work together with one or two fellow students, directly applying your new skills in the project. The project is concluded with two reports on progress and outcomes (maps, charts, etc.).

Performance engineering is the area of computer science ensuring that computer systems (comprised of hardware as well as software) are responsive, scalable, and efficient.  The course introduces fundamental principles and techniques used in performance management of modern computer systems, either purpose-built applications or generic system (single-node as well as distributed). 

Network science provides powerful tools for modeling and understanding complex systems, and offers data-driven approaches to uncovering their underlying structures and dynamics. This course introduces students to fundamental statistical methods with a particular focus on their application within network science. It provides a comprehensive foundation in the principles and techniques essential for network modeling, analysis, and statistical inference in complex networks.

This course provides analytical tools to understand the causes and consequences of the digital revolution. The course first examines its historical and technical foundations, then analyzes the economic and social transformations it has brought about. These perspectives enable students to critically reflect on their own digital practices in light of broader societal changes and their implications for individuals and societies. This reflection is further developed through independent reading of recent books on the topic.

This course presents the systems and hardware foundations of modern machine learning, bridging neural network workloads, compiler optimizations, and advanced AI hardware. The course covers cutting-edge AI hardware, including GPUs, TPUs, and wafer-scale accelerators, highlighting the design trade-offs between cloud and edge deployment. Topics such as efficient ML (quantization, pruning), algorithm–hardware co-design, federated learning, and system support for large language models (LLMs) are also presented. Through lectures, tutorials, and hands-on programming lab sessions, students gain practical skills and a systems-level understanding of ML deployment, preparing them for advanced research or industry roles in ML system design.

This course addresses the fundamental concepts and advanced methodologies of deep learning and relates them to real-world problems in a variety of domains. It provides an overview of different approaches, both classical and emerging. The course equips students with the necessary knowledge and skills to work in the field of deep learning and to contribute to ongoing research in the area.

Students explore quantitative mathematical methods for taking decisions in the presence of constraints or finite resources; learn about linear programming, integer linear programming, robust optimization, and game theory and their application; classify mathematical programs on the basis of the number and types of their solutions; implement solution techniques for linear programs with both real and integer-valued variables; and become familiar with fundamental notions of duality, degeneracy, and sensitivity.

What puts former criminals on the right track? How can we prevent heart disease? Can Twitter predict election outcomes? What does a violent brain look like? How many social classes does 21st century society have? Are hospitals spending too much on health care, or too little? Data analysis is the art and science of tackling questions like these by looking at data. Just as cartographers make maps to see what a country looks like, data analysts explore the hidden structures of data by creating informative pictures and summarizing relationships among variables. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict important events and variables so we can act on this knowledge. Methods from statistics, machine learning, and data mining play an important part in this process, as well as visualizations that allow the analyst and other humans to better understand what we can conclude from the available facts. During this course, students actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualizing techniques. The course goes beyond linear and logistic regression and thus continue where “Fundamental techniques in data science with R” ended. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, student gain hands on experience in using them on real data during the course and interpreting the results. Entry requirements include at least followed an introductory statistics course of 7.5 EC, and familiarity with correlation and regression, comparing means and cross tabulations of categorical variables. It's also expected to have hands on experience in carrying out these analyses, with, for example, SPSS, Stata, R or SAS.

This course begins with an introduction to the evolution of computers and their operating principles. It then gradually guides students to become familiar with programming structures and application design processes, including: basic syntax, flow control, exception handling, input/output, and classes. The course topics are as follows: Introduction Python Basics Flow Control Functions Sequence, Dictionary, and Set Array-Oriented Programming with NumPy File, and Exception Object-Oriented Programming.

In this course students have the opportunity to explore this exciting topic that sits at the boundaries of computer science and physics. The course is taught from a computer science perspective, but it also draws on topics from linear algebra, which provides the mathematical apparatus for formalizing quantum systems. Students are introduced to the basic notions of quantum computing, including quantum bits and quantum entanglement and explore quantum algorithms, such as Quantum Search, Quantum Simulation and Quantum Information concepts such as quantum error correction.