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Utrecht Summerschool ‘AI for non-AI students’

| Sieuwert van Otterloo | Artificial Intelligence
HU summerschool 2026

Every year the AI research group of the Utrecht University of Applied Sciences organises a summer school for international students that want to visit Utrecht and learn about a new research field. The course is open for students from all fields, and was attended this year by 15 students with backgrounds in law, economics, engineering, mathematics and business communication. In five days, these students learned what AI is and how it can be used, but also about risks and regulation. In case you missed it, this article contains links to the notebooks, papers, presentations and datasets used in the summer school.

How to start with AI

The morning program of this week long summer school was focused on learning what AI is and how AI models and algorithms are made. This was done using the Python programming language, open datasets from Kaggle and multiple common algorithms, like linear regression, decision trees and neural networks. On Monday, students focused on exploring and visualising data from the main example dataset: the Utrecht Housing Dataset. On Tuesday, several traditional, simpler machine learning techniques were used: linear regression, decision trees and random forests. These techniques are often used in practice because they are fast to use and give predictable and explainable results. Later in the week we focused on neural networks and evolutionary algorithms. Together this gives a good idea of machine learning and AI, and how any researcher can use AI on their own data.

Applications and Risks of AI

In the summer school we also explored the latest trends and applications. Our guest speaker and researcher Tina Mioch ,researcher focused on AI for emergency responders, showed how computer vision and AI can be applied to save people by helping first responders to automatically recognise humans from drone live footage.  Tina van der Linden, researcher into ethical and  legal aspects for AI such as use of GenAI in education, discussed with students how the law can help us address the risk of AI (and influence from Big Tech) in a grounded discussion at the intersection of technology and regulation. We further talked about principles of redress, biases, and group fairness in the context of fairness and how AI can condition our lives. To make it practical, all students played a serious game about price discrimination in car insurance that is available online and you can play yourself. .

drone footage

Long-Range Human Detection in Drone Camera Images – J. Heemskerk, T. Mioch, H. Maathuis and H. Aldewereld – ISCRAM 2024

New trends in AI

Sietske Tacoma explained and illustrated how textual information can be embedded into multidimensional vectors to be efficiently processed with Large Language Models (LLMs). Participants have experienced first hand how meaning in text is embedded and use in neural networks weights that make up the overall LLM architecture. Speaking of LLMs, Pavlo Burda took the participants through a practical tour of LLMs’ usage including latest trends and connected risks. Among case fabrications and advice to “eat rocks”, we tried to let models hallucinate and replicate findings on how human can(not) detect AI-generated images. A prompt engineering session culminated with participants applying the lates techniques on agentic coding by developing a browser-based arcade game.

LLM tools

Popular open-weight local LLMs in 2026 according to onyx.app and a collection of tools for for local AI deployments and pipeline engineering.

From first-steps with Python for data science, to fine-tuning Artificial Neural Networks (ANNs) with the help of teachers and teaching assistants, every morning we provided focused practical exercises using Python notebooks that implement the theoretical explanations. During the hands-on practical sessions we experimented with prompt and context engineering by tackling simple Retrieval Augmented Generation (RAG) challenges, exploring the Hugging Face model repository to find local LLMs to deploy with AnythingLLM and experiencing agentic coding with tools like Cursor.

Recommended reading

For the whole duration of the summer school, and as a take away, we provided and discussed several articles and guides on AI. These resources have been compiled for every days of the 2026 program and as a further reading to deepen a given concept or exercise after a day session. All resources have been collected from open sources, maximizing the availability of the content for everyone.

Booklet

The participants received a booklet with the following chapters and articles:

Image: taken by Sieuwert van Otterloo. The HU logo was inserted using AI. 

Author: Sieuwert van Otterloo
Dr. Sieuwert van Otterloo is a court-certified IT expert with interests in agile, security, software research and IT-contracts. He is a also an ISO 27001 and NEN 7510 auditor and AI researcher.