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 2027

Artificial Intelligence

Course content 

For official course catalogue information check the course catalogue: Course Catalogue 2026-2027 (available from june 2026). 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

X-perience

This course fosters students’ personal and professional growth in a rapidly evolving IT landscape. Students cultivate an entrepreneurial mindset, seize initiatives, and proactively engage with emerging technologies and application domains. They expand their knowledge, skills, and attitudes beyond the core curriculum, gaining a broader perspective on IT in business and international contexts. 

Through activities in internationalization, innovation, personal development, and student engagement, students hone their professional attitude and communication skills. They adopt a systematic, project-based approach, demonstrating curiosity, empathy, and hands-on learning. 

A central element is building an e-portfolio, where students compile evidence of their progress, critically reflect on their learning journey, and outline a clear personal development plan. This process helps them recognize their evolution as emerging IT professionals and pinpoint areas for growth. 

By course end, students can confidently position themselves in a dynamic sector, communicate effectively with internal and external stakeholders, and contribute meaningfully to the wider IT community. 

Security Advanced

This course provides an in‑depth, practice‑oriented exploration of advanced cybersecurity domains within an enterprise context. Students learn to classify organizational security needs using the infosec color wheel and to relate different security roles and activities to concrete business risks. 

Core topics include Open‑Source Intelligence (OSINT), reverse engineering, malware analysis and deception, web application vulnerabilities (such as cross‑site scripting and injection attacks, aligned with the OWASP guidelines), digital forensics, incident response, penetration testing, mobile and API security (including OAuth 2.0), and secure coding practices. 

Throughout the course, students work in realistic lab environments where they identify, exploit, and mitigate vulnerabilities in a controlled and ethical manner, applying both offensive and defensive techniques. They independently research potential attack vectors, implement technical countermeasures, and document their findings in professional reports. Team‑based assignments strengthen their ability to collaborate across security functions, communicate risks clearly to different stakeholders, and reflect on the role of each color in the infosec color wheel. 

By the end of the course, students have a solid technical foundation in multiple cybersecurity disciplines and the practical skills needed to operate effectively in an enterprise security team.

Data Advanced

This course deepens students’ mastery of modern data technologies by integrating procedural programming in relational databases with foundational Big Data and NoSQL concepts. 

Students build robust database applications using PL/SQL in Oracle, covering stored procedures, functions, triggers, packages, control structures, cursors, and exception handling to create reusable, secure transaction logic within autonomous SQL blocks. They gain skills in designing, debugging, and maintaining complex PL/SQL code while ensuring data integrity and performance.  

The course then shifts to Big Data fundamentals, including the 3 V’s (Volume, Velocity, Variety), CAP theorem, distributed file systems (e.g., HDFS), and a comparison of RDBMS with NoSQL paradigms, with in‑depth focus on document‑oriented databases like MongoDB. Practical exercises involve constructing advanced FIND queries (with projections, sorting, limits, and aggregation) and performing insert, update, save, and remove operations on collections. 

By the end, students can evaluate data technologies for specific use cases, leverage their strengths and mitigate limitations, and proficiently develop solutions across relational and NoSQL environments. 

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.