Explanatory Data Analysis group

Teaching

Our group is involved in teaching the following courses.


Probability Theory for Computer Scientists (25-26,24-25)
Level
BSc, 6 EC
Part of
BSc Data Science & Artificial Intelligence, BSc Computer Science
Lecturers
Saber Salehkaleybar, Christos Athanasiadis
Contents
In this course, students learn how to model, quantify, and analyze uncertainty. The fundamental tools of probability will be covered which are essential to analyze and make sense of data. The course will focus on introducing basic concepts and methodologies, and will contain multiple examples and real-world applications.
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Statistics for Computer Scientists (25-26,24-25,23-24,22-23,21-22,20-21,19-20,18-19,17-18,16-17)
Level
BSc, 6 EC
Part of
BSc Data Science & Artificial Intelligence, BSc Computer Science, minor Data Science
Lecturers
Matthijs van Leeuwen, Marieke Vinkenoog
Contents
In this course, we cover the basics of statistics, the fundamental 'data science' that researches the description and analysis of data. The focus is on learning how to correctly apply statistical methods, not on their mathematical justification.
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Causal Inference for Computer Scientists (25-26,24-25)
Level
MSc, 6 EC
Part of
MSc Computer Science
Lecturers
Saber Salehkaleybar
Contents
In the field of machine learning, you may need to predict the impact of an action on an outcome by analyzing a huge amount of collected data. Unfortunately, this data is often of low quality, with missing records, unobserved confounders, and selection biases. As a result, answering questions about the impact of an action becomes more difficult than ever. This course covers mathematical tools to help you perform causal inference in big data. We will use structural causal models, and potential outcomes to formalize what causal effects mean, explain how to express these effects as functions of observed data, and use machine learning techniques to estimate them.
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Information Theoretic Data Mining (25-26,24-25,23-24,22-23,21-22,19-20,18-19,17-18)
Level
MSc, 6 EC
Part of
MSc Computer Science
Lecturers
Matthijs van Leeuwen, Francesco Bariatti
Contents
How can we gain insight from data? How can we discover and explain structure in data if we don't know what to expect? What is the optimal model for our data? How do we develop principled algorithms for exploratory data mining? To answer these questions, we study and discuss the state of the art in the relatively young research area of information theoretic data mining. We focus on theory, problems, and algorithms, not on implementation and experimentation.
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Data Science (Honours Class; 25-26,24-25,23-24,22-23,18-19,17-18,16-17)
Level
BSc, 5 EC
Part of
Honours Classes
Lecturers
Marieke Vinkenoog and guest lecturers
Contents
Data Science deals with handling, processing, analyzing, interpreting, and extracting knowledge from data, ultimately to derive optimal decisions. Today, it is of paramount importance in just about any domain, ranging from the life sciences, including e.g., health and biosciences, to banking, insurances, retail, and heavy industries. This Honours Class first introduces students to some of the fundamental concepts and then continues with overviews of specific application domains.

Methodology and Research Approach (16-17)
Level
MSc Post Experience, 6 EC
Part of
ICT in Business
Lecturers
Matthijs van Leeuwen, Mirjam van Reisen
Contents
The aim of this course is for the student to get both conceptual insight into and practical experience with the different steps involved in conducting research. This includes, for example, the development of a research question, research design, data collection and analysis, drawing conclusions. Part of the course concerns a primer on statistics, including both descriptive and inferential statistics.

Applied Statistics (15-16)
Level
BSc, 4 EC
Part of
Computer Science & Economy, minor Data Science
Lecturers
Matthijs van Leeuwen, Hendrik Blockeel
Contents
This was a reduced version of the current Statistics course.