Applied Computer Science AUTUMN

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.

AUTUMN 2026

Artificial Intelligence

Course content 

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

IT Project

In this course, students work in teams on a real‑world, industry‑based project to design, develop, and deliver a complete software solution. They go through the full project lifecycle—from analysis and modelling to programming, integration, and delivery—while responding to authentic business requirements. Students integrate their own research, new technologies, and client feedback into existing models and codebases, and manage a realistic project plan. Professional collaboration and communication are central: students present progress to both technical and non‑technical audiences and demonstrate a professional attitude in planning, reporting, and teamwork.

MLOps

This course covers the core principles and practices of MLOps for bringing machine‑learning models into reliable, scalable, and maintainable production environments. Students explore the full MLOps lifecycle and apply processes such as Continuous Integration, Delivery, Testing, and Monitoring in hands‑on cases. They work with common MLOps tools, version control, automated testing, and model release management to design and maintain production‑ready pipelines. By the end, students can set up and sustain a complete MLOps workflow that bridges experimental ML models and real‑world deployment.

Cloud for AI

This course introduces the essential concepts of cloud computing for AI‑driven solutions. Students explore core services of major cloud providers and learn to choose appropriate tools for AI use cases. Through hands‑on labs and a project, they design, implement, deploy, and maintain machine‑learning models in the cloud, with attention to scalability, performance, cost management, security, governance, and compliance. By the end, students can architect and evaluate production‑ready AI systems in the cloud that align closely with MLOps practices.

Big Data

This course introduces the core concepts of Big Data and situates large‑scale data processing within AI and Data Science. Students work with modern Big Data ecosystems and tools such as Apache Spark, Kafka, NiFi, and time‑series databases. They iteratively build and adapt a scalable data pipeline for a concrete use case, applying machine‑learning and recommendation techniques (e.g. clustering, recommender systems) within Big Data frameworks. By the end, students can design and implement Big Data solutions that power AI‑driven applications.

Neural Networks

This course provides an in‑depth introduction to Neural Networks and Deep Learning within the broader field of Artificial Intelligence. Using frameworks such as FastAI/PyTorch and Keras/TensorFlow, students learn how neural networks work and apply them to real‑world problems. Topics include model design, initialization, optimization, regularization, and performance evaluation on datasets from Computer Vision, Natural Language Processing, time‑series, and unsupervised deep learning (autoencoders, GANs, diffusion models). The course also introduces Explainable AI and ethical reflection, enabling students to design, implement, and critically assess deep‑learning systems across domains.

Smart Devices

This course introduces the essential concepts of robotics and the practical use of the Robot Operating System (ROS). Students develop robotic prototype systems, control different robot types (e.g. AMRs, UAVs, humanoids), and implement Simultaneous Localization and Mapping (SLAM) for autonomous navigation in unknown environments. They further apply Reinforcement Learning so robots can learn optimal behaviours through interaction. By the end, students can design and refine intelligent robotic systems that combine ROS, SLAM, and reinforcement learning to solve autonomous‑robot challenges.

Trends in AI

This course trains students to identify, analyse, and critically assess emerging trends in innovative AI software projects. Using frameworks such as the Gartner Hype Cycle, Sustainable Development Goals, and Technology Readiness Levels, they position AI trends in a broader technological and societal context. Students systematically collect and share up‑to‑date knowledge, evaluate business value and adoption, and discuss real‑world impact in class and through hands‑on research. By the end, they can investigate cutting‑edge AI innovations and judge their practical and economic relevance for modern software projects.