International Summer School: Trustworthy AI – Machine Learning Meets Blockchain

This microcredential provides an introduction to the fundamental concepts and practical applications of distributed ledger technologies (DLT), including blockchain, smart contracts, and NFTs. Participants will gain both theoretical and hands-on experience by developing and deploying basic smart contracts using Java or TypeScript.

Key topics include:

• History and background of Bitcoin and blockchain technologies
• Technical structure and implementation of blockchains and mining
• Tokens and NFTs
• Integration of public and private distributed networks
• Smart contracts, Solidity, and the Ethereum Virtual Machine (EVM)
• Digital identities and Zero-Knowledge Proofs

The course combines lectures, discussions, and programming exercises to provide applied understanding and skills in blockchain development.

This microcredential offers an in-depth exploration of advanced methods in machine learning with a focus on computer vision and medical image analysis. It combines theoretical insights with practical programming exercises in Python and PyTorch.

Participants will learn how to design, train, and evaluate modern ML models, understand their theoretical foundations, and apply them to real-world data.

Key topics include:

• Foundations of machine learning and model evaluation
• Computer vision fundamentals and image processing
• Deep learning architectures (CNNs, Vision Transformers)
• Transfer learning and domain adaptation
• Medical image analysis and data challenges
• Research trends: foundation models, multimodal learning, and trustworthy AI

Throughout the course, students work in teams on a small-scale ML project involving medical image data, culminating in a presentation of results and methodological insights.

• Explain the technical principles of distributed ledger technologies.
• Understand blockchain, Web3, NFT, and crypto concepts.
• Interact programmatically with distributed ledgers.
• Develop and deploy basic smart contracts using Solidity.

• Understand and apply advanced machine learning concepts and their theoretical foundations.
• Identify and match appropriate ML methods to data types and problem settings.
• Describe and implement computer-vision architectures such as CNNs and Vision Transformers.
• Use PyTorch to design, train, and evaluate ML models.
• Critically assess model performance using theoretical and empirical evidence.

The summer school takes place on the campus of OTH Regensburg at the Faculty of Computer Science and Mathematics in Regensburg, Bavaria, Germany.

Regensburg is both a vibrant student city, offering a wide range of cultural activities and a large numbers of hotels, cafés, bars and restaurants, and a UNESCO World Heritage site thanks to its exceptional medieval oldtown.

Both international students and students from OTH Regensburg can participate in the summer school. Thus, participants experience an international atmosphere and get in touch with German culture.

With small cohorts of approximately 30 participants per group the summer school allows for a close interaction with instructors and peers.

The summer school consists of two microcredentials, each comprising 3 ECTS credits. The total workload is 25 hours per ECTS credits, including on-site teaching units and self-study during the summer school.

• Solid knowledge of software development in Java or TypeScript and basic familiarity with Git/GitHub.
• Solid programming skills in Python (syntax, data structures, functions).
• Basic knowledge of linear algebra, probability, and statistics.
• Familiarity with NumPy, pandas, and matplotlib.
• Willingness to work with Jupyter Notebooks and PyTorch.
• English skills on the level B2.

Students in computer science, information systems, data science or related fields who wish to acquire applied knowledge of distributed ledger and blockchain technologies and who seek to deepen their understanding of machine learning and apply modern AI techniques to real-world problems.

The assessment consists of a team-based ML project and presentation during the summer school, and an individual programming project involving the implementation of a distributed ledger application. Upon successful completion, participants receive the two academic microcredentials "Machine Learning” and "Distributed Ledger Technology". For each microcredential, participants are awarded an official certificate with 3 ECTS credits by OTH Regensburg.