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Zoekresultaten (17)

21 juli 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

8 april 2026

ACA-bot

ACA-bot is an engaging chatbot designed for Automated Conversational Assessment (ACA). Drawing inspiration from traditional oral exams, this text-based conversational agent transforms assessment into a dynamic learning experience. It supports both formative and summative evaluation by offering students personalised feedback and guidance at every step of their learning journey. The first ACA-bot prototype focuses on assessing students’ understanding of basic statistical concepts, but its flexible design allows for easy adaptation to a wide range of subjects and learning contexts. Through interactive dialogue, students can practise explaining ideas clearly under exam-like conditions, apply their knowledge to new and challenging questions, and refine their reasoning as they respond to follow-up prompts. They gain valuable insights into their own understanding, identify areas for improvement, and gradually build confidence for both oral and written assessments. For educators, ACA-bot offers a powerful way to scale oral examinations and enhance formative assessment without increasing workload. It automates parts of the assessment process, provides structured and actionable insights into student performance, and opens the door to more personal, interactive feedback at scale. By combining the strengths of conversational AI and educational assessment, ACA-bot turns evaluation into a collaborative, insightful process. It empowers students to think aloud, reason more effectively, and grow, while giving educators a smarter, more innovative way to understand and support learning. ACA-bot is een slimme en inspirerende chatbot die onderwijs en Artificial Intelligence samenbrengt. Geïnspireerd door het klassieke mondelinge examen tilt deze tekstgebaseerde gespreksagent Automated Conversational Assessment naar een nieuw niveau. Studenten krijgen via natuurlijke gesprekken directe, persoonlijke feedback en begeleiding, waardoor leren en toetsen samenvloeien in één boeiende ervaring. Het prototype van ACA-bot is ontwikkeld om kennis over statistiek te beoordelen, maar dankzij het flexibele ontwerp kan het systeem eenvoudig worden aangepast aan andere vakgebieden en toepassingen. Tijdens interactieve dialogen leren studenten helder uitleg geven, hun kennis toepassen op nieuwe vragen en hun redenering aanscherpen. Ze ontdekken waar hun begrip nog verdiept kan worden en groeien zichtbaar in zelfvertrouwen — waardevolle voorbereiding op zowel mondelinge als schriftelijke examens. Ook voor docenten biedt ACA-bot een slimme oplossing. Het maakt mondelinge toetsing schaalbaar, ondersteunt formatieve beoordeling via gesprek, en vermindert de werkdruk door delen van het beoordelingsproces te automatiseren. Bovendien geeft het gestructureerde inzichten in de voortgang en denkwijze van studenten, waardoor elke dialoog bijdraagt aan beter onderwijs. ACA-bot laat zien hoe Automated Conversational Assessment het leren persoonlijker, interactiever en effectiever maakt. Studenten denken hardop, verkennen hun eigen begrip, en verbeteren met vertrouwen — terwijl docenten op een innovatieve, datagedreven manier inzicht krijgen in hoe kennis werkelijk wordt begrepen.

App

Aki Härmä, Julie Bauer

3 maart 2026

Crash course SPSS syntax

Standalone assignment. A very basic, step-by-step introduction to SPSS and SPSS syntax. Uses data from the European Social Survey as example.

Document

Alex Lehr

6 juni 2025

Multilevel modeling for social scientists

The course builds on the knowledge and skills acquired in the previous methods courses. The course will introduce students to multivariate analyses of nested data using multilevel modeling techniques. The course focuses on the theory and application of fixed and, in particular, random effects models. Using data from the European Social Survey, the students will learn how to estimate and interpret such as models using Stata.

Document

Alex Lehr

12 juni 2024

Probabalistic Analysis of Classical Oscillator

This virtual lab serves as a bridge from classical mechanics to the inherently probabilistic nature of quantum theory. It allows students to analyze a classical system, here a block oscillating on a spring, in a probabilistic way. This is achieved through a photographer that takes snapshots (photos) of the oscillating block at random times. The position of the block is binned, building up a histogram of snapshot numbers versus position bins. Students can change the bin size, and convert the snapshot histogram into a probability density histogram. They can also display the theoretical probability density histogram, as well as the theoretical probability density curve as the bin size tends to zero. Students can see that the more measurements they take, the smaller on average the deviations of their data from the theoretical values. A second tab allows students to solve a number of challenges. The challenges probe the primary aims outlined below. Primary aims: Students can describe the experimental procedure shown in the lab for analyzing a classical system in a probabilistic way. Students can describe qualitatively how the block’s speed in a given region relates to the number of snapshots in that region. Students understand the difference between a probability and a probability density. They can perform calculations to find probabilities and probability densities from the number of snapshots in a given region.

WoWebsite

Antje Kohnle, Mark Paetkau

2 november 2023

LU - PE3 - Assignment

This file contains a file on how to write a research proposal, an inspirational matrix to help students conceive a first idea of their project. It is followed by the project requirements and a file on writing good physics research questions. A LaTeX template for an article is added as well. Students have previous experience in writing both research proposals and articles from their first year lab course.

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Paul Logman

23 augustus 2023

Lecture: Understanding data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

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HCAIM Consortium

23 augustus 2023

Lecture: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

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HCAIM Consortium

23 augustus 2023

Tutorial: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

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HCAIM Consortium

23 augustus 2023

Practical: SVMS and Kernels

To be able to design and use a linear SVM To be able to design ad use a non-linear SVM, using a kernel To design and optmize a SVM (cookbook approach)

Website

HCAIM Consortium

HBO/WO

MBO