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HBO/WO

MBO

2 februari 2026

Scholingstraject AI-geletterdheid (UBVU)

Deze collectie bestaat uit verschillende materialen van het scholingstraject AI-geletterdheid. De doelgroep van dit specifieke traject is medewerkers bij de Universiteitsbibliotheek VU. Je kan het materiaal ook met eventueel wat aanpassingen ook voor andere doelgroepen gebruiken. Het scholingstraject bestaat uit 4 sessies: Introductie in AI Toelichting: Algemene introductie over wat AI is. Welke begrippen moet je weet van hebben? Wat kan een taalmodel? Etc. Verder diende deze sessie om het beginniveau van collega’s te bepalen, zodat wij hier rekening mee konden houden voor de opvolgende sessies. Effectief prompten Toelichting: Dit is een praktisch onderdeel, met als doel op een effectieve manier te leren prompten om de juiste informatie te verkrijgen. Verantwoord & kritisch omgaan met AI Toelichting: Dit is een meer theoretisch onderdeel met veel ruimte voor gesprek en discussie, over onderwerpen als: duurzaamheid, big tech, etc. AI-tools & werk Toelichting: In deze sessie nodigden wij 3 collega’s uit om hun ervaringen te delen en een demo te geven hoe zij AI gebruiken in hun werk. In deze collectie vind je de slides met inhoudelijke stof en interactieve opdrachten, inclusief een PDF voor de gebruikte exit ticket en toetsmatrijs (met leerdoelen gekoppeld aan de taxonomie van Bloom). Verder is ook een link naar de e-learning ‘AI-geletterdheid voor UB-medewerkers’ opgenomen. Deze werd gebruikt als aanvulling (voor- en/of nabereiding) op de fysieke sessies. Als laatste is een eindopdracht toegevoegd met bijbehorende rubric.

Document

Kim Dibbets, Jordy Gevers

2 juli 2025

Data Analysis and Visualization using R

Course is in English Learning outcomes: Apply Core R Programming Skills Learners will be able to write and execute R scripts using basic and complex data types, utilize built-in functions, and implement flow control structures to automate data analysis tasks. 2. Import, Clean, and Manipulate Data Learners will demonstrate the ability to read various data file formats, tidy and transform dataframes using both traditional methods and modern packages such as tidyr and dplyr, and handle date-time and text data efficiently. 3. Visualize Data Effectively Learners will create and customize data visualizations using the ggplot2 package, including revisiting advanced plotting techniques and adding interactivity to graphical outputs. 4. Perform Exploratory Data Analysis (EDA) Learners will conduct EDA using R, applying appropriate statistical summaries and visualizations to uncover patterns, trends, and insights from datasets. 5. Utilize R Packages and Resources Independently Learners will identify, install, and apply relevant R packages to extend functionality, troubleshoot code, and solve data analysis problems, including working through exercises and interpreting provided solutions. Duration Consists of 18 lessons Study points: 5 EC

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Michiel Noback

10 maart 2025

EPIFFANI Project Results

These are the combined results for the EPIFFANI Project by Prof. Nicolas Mangin. This project was funded buy SURF's Stimuleringsregeling open en online onderwijs. The aim of the project was to build and test a set of four open source applications for R/integration into the LMS, which allowed teachers to easily automate formative testing through IT solutions. With these applications, teachers can easily and quickly create automate the processing of making tests with personalized feedback for students. The results have three elements: A full project report, which provides context for the creation and creation process A link to Youtube video demonstration of the tools, made for the University of Groningen's education festival 2022 A link to the Github page of project lead Nicolas Mangin. On this page, all the packages can be downloaded, and documentation and instruction are available All tools are licensed under a GPL-3 license, but as this license is not available, it has been noted as CC BY-SA here.

Document

Nicolas Mangin

23 augustus 2023

Tutorial: Data architecture

To know the fundamentals of Machine Learning System architectures To know how to design and automate a Machine Learning pipeline To know the basic aspects of some ML production pipeline To know how to configure a ML production pipeline on Cloud

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HCAIM Consortium

23 augustus 2023

Interactive session: Data architecture

To know the basic data architectures in Machine Learning Pose questions about the most suited data architectures

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HCAIM Consortium

23 augustus 2023

Lecture: Batch processing

Understand mechanisms behind batch processing and back propagation Gradient Descent, Batch vs MiniBatch vs Epoch

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HCAIM Consortium

23 augustus 2023

Lecture: CI/CD

Students will gain an overview of DevOps Students will be introduced to CI/CD, including what happens in CI and in CD Students will develop testing in a CI/CD pipeline Students will develop Github actions

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HCAIM Consortium

23 augustus 2023

Lecture: Trust, Normativity and Model Drift

Understand the need to measure trust in AI Understanding the concept of Normativity in context with Future AI Explain various kinds of model drift and recommend timely measures for future AI systems to address model drift

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HCAIM Consortium

23 augustus 2023

Lecture: EU proposal of Regulation on HCAI applications

The objective of the course is to introduce the students to the legal environment of AI, including especially the basic principles and guidelines and the present and possible future framework of these laws, especially the Artificial Intelligence Act of the EU. The lecture helps recognise and mitigate the risks, know the accountability and governance implications of AI, and what we need to do to ensure lawfulness, fairness, and transparency in AI systems.

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HCAIM Consortium

30 juni 2023

Search methods & techniques: Google vs library resources?

Should I be using Google or the library resources? Unfortunately, it’s not simply a matter of choosing one or the other. Internet search engines like Google can search billions of public web pages for your keywords in seconds, but they cannot access proprietary data. Google vs PsycINFO Why do you have to use an academic database, like Scopus or PsycINFO? Is Google Scholar, a simple way to broadly search for scholarly literature, not enough? To let you experience the differences between Google Scholar and academic database, an example. Related.

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Erasmus University Library