Applied Computer Science SPRING

Exchange courses in Applied Computer Science

An English taught programme for international exchange students who have obtained at least 60 ECTS in the study field of Applied Computer Science, on bachelor level.

SPRING 2025

Artificial Intelligence

Course content 

For official course catalogue information check the English Study Guide (available from june). Below you can find a description of the course contents. 

Machine Learning

You’ll learn the basic principles in the domain of Machine Learning. As data is the main resource in this domain, you will learn to gather, understand and process data from different sources. Data visualization is an important topic and is covered as well in this course. Some necessary mathematical components are covered to be able to understand the workings of all covered mechanics in Machine Learning.
In a group project, you’ll use the gathered knowledge to create a working data solution.

• Essential concepts in Machine Learning
• Data collection and data analysis
• Data visualization
• Data quality & data cleaning
• Structured & unstructured data
• Supervised learning
• Unsupervised learning
• Evaluation of AI solutions

AI Algorithms and Computer Vision  

During this course, common solutions for classical AI problems will be tackled. The course serves as an introduction to classical artificial intelligence and computer vision. You will learn about fundamental data structures, time- and space-complexity and essential algorithms. In the domain of computer vision, all basic operations will be covered, so you’ll be able to preprocess image data for further use in all kinds of AI applications.

In a group project, you’ll use the gathered knowledge to create a working AI solution.

• Multiple concepts within classical AI

• Solve AI problems with algorithmic solutions

• Relevant data structures and search algorithms

• Compare solutions using time- and space-complexity

• Analyze and process image data with basic computer vision techniques

Web for AI

In this course, you’ll learn to create (responsive) web applications, using popular frontend frameworks (React, Vue, Angular, Bootstrap, …) You will be able to create a prototype to test or showcase an AI-application using web technologies.

Furthermore, you will learn to communicate with external REST APIs to enrich web applications with AI services. Finally, you will be able to enable your own AI solutions through a custom-made REST API in Python, so they can be used in other web applications.

• Responsive web applications with CSS frameworks (Bootstrap)
• Web applications with JavaScript framework(s)
• Communicate with external REST APIs
• Explore existing AI web services and learn to integrate them in custom web applications
• Create RESTful web services with Python framework(s)
• Integrate own (AI) solutions in a custom REST API
• Combine all of the above to create a functional web application

Java Expert

• Building a Restful APIs with Spring Boot and Maven

• Writing generic algorithms

• Spring Security

• Spring Boot Unit Testing with Mockito and MockMvc

• Advanced topics in JPA (Jakarta/Java Persistence API)

• Servlet technology

• Multithreading

• Relevant Java and Spring Boot topics from blogs, Java Magazine, …

• Project

.NET Expert

• C# Language Features

• WPF and XAML

• MVVM

• Advanced concepts of the Framework

• TDD (Test-driven development)

Research Project AIN

You are part of a group of several students. It is your task to deliver a working AI application based on a problem description. These assignments are set up in such a way that they correspond to what the students are taught in the course 'AI Algorithms and Computer Vision', 'Web for AI' and 'Machine Learning'.This knowledge is applied in a concrete project with an emphasis on Rapid Prototyping. 

The project team uses agile methodology to streamline the process throughout several sprints. The project runs throughout the semester and is divided into a number of work packages, including analysis, design, planning, research, implementation, documentation and presentation.

In the project week and this course, the following topics regarding professional and personal development are discussed through different workshops: how to communicate - feedback rules, group dynamics, time / self-management and conflict management within teams.