## Topics in Algorithmic Data Analysis (SS'24)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we will investigate hot topics in machine learning that the lecturer thinks are cool. We will (probably) look into how to draw causal conclusions from data, how to discover insightful patterns and dependencies, how to learn inherently interpretable models, and many other amazingly interesting topics.

# Recent

more ▾## Elements of Machine Learning (WS'23)

**Basic Lecture** *and* **Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Krikamol Muandet

In this course we discuss the foundations – the elements – of machine learning. In particular, we focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects are covered.

## Topics in Algorithmic Data Analysis (SS'23)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we will investigate hot topics in data mining and machine learning that the lecturer thinks are cool. We will probably look into how to draw causal conclusions from data, how to discover significant and useful patterns from different types of data, and other amazingly interesting topics.

## Don't Panic – or – How to Survive a PhD (SS'23)

**Special Lecture** (0 ECTS).
Lectured by Jilles Vreeken

Pursuing a PhD can be daunting, scary, stressful, and otherwise tricky business. With a bit of preparation, a few tips and tricks, and a dash of luck, pursuing a PhD can be awesome. In this talk I will give part of that preparation and share the tips and tricks I know of.

## Elements of Machine Learning (WS'22)

**Basic Lecture** *and* **Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Aleksandar Bojchevski

In this course we discuss the foundations – the elements – of machine learning. In particular, we focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects are covered.

## Topics in Algorithmic Data Analysis (SS'22)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to draw causal conclusions from data, how to discover significant and useful patterns from different types of data, and other amazingly interesting topics.

## Elements of Machine Learning (WS'21)

**Basic Lecture** *and* **Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Isabel Valera

In this course we discussed the foundations – the elements – of machine learning. In particular, we focused on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects were covered. This course is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling.

## Topics in Algorithmic Data Analysis (SS'21)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to discover significant and useful patterns from data, determine causal relations, and other amazingly interesting topics.

## Elements of Machine Learning (WS'20)

**Basic Lecture** *and* **Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Isabel Valera

In this course we discussed the foundations – the elements – of machine learning. In particular, we focused on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects were covered.

## Topics in Algorithmic Data Analysis (SS'20)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to discover significant and useful patterns from data, determine causal relations, and other amazingly interesting topics.

## Elements of Statistical Learning (WS'19)

**Advanced Lecture** (5 ECTS).
Lectured by Jilles Vreeken and Tobias Marschall

In this course we conveyed the ability, given a data set, to choose an appropriate statistical method for analyzing it, to select the appropriate parameters for the statistical model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects were covered.

## Don't Panic – or – How to Survive a PhD (WS'19)

**Special Lecture** (0 ECTS).
Lectured by Jilles Vreeken

Pursuing a PhD can be daunting, scary, stressful, and otherwise tricky business. With a bit of preparation, a few tips and tricks, and a dash of luck, pursuing a PhD can be awesome. In this talk I gave part of that preparation, and shared the tips and tricks I know of.

## Topics in Algorithmic Data Analysis (SS'19)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to discover significant and useful patterns from data, determine causal relations, and other amazingly interesting topics.

## Elements of Statistical Learning (WS'18)

**Advanced Lecture** (5 ECTS).
Lectured by Jilles Vreeken and Tobias Marschall

In this course we conveyed the ability, given a data set, to choose an appropriate statistical method for analyzing it, to select the appropriate parameters for the statistical model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects were covered.

## Topics in Algorithmic Data Analysis (SS'18)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to discover significant and useful patterns from data, efficiently measure non-linear correlations, determine causal relations, as well as how to analyse large graphs.

## Information Retrieval and Data Mining (WS'17)

**Core Lecture** (9 ECTS).
Lectured by Jilles Vreeken and Jannik Strötgen

Information Retrieval (IR) and Data Mining (DM) are methodologies for organizing, searching and analyzing digital contents. In this rendition of the course we particularly looked into extracting knowledge from structured data (eg. tables, sequences, graphs) as well as from unstructured data (text, web), and how to make good use of the knowledge we so discover. Topics we covered included text indexing, search result ranking, and information extraction for semantic search, as well as pattern mining, clustering, classification and recommendation.

## Topics in Algorithmic Data Analysis (SS'17)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

In this advanced course we investigated hot topics in data mining and machine learning that the lecturer thinks are cool. We looked into how to discover significant and useful patterns from data, efficiently measure non-linear correlations, determine causal relations, as well as how to analyse large graphs.

## Subgroup Discovery (SS'17)

**Seminar** (7 ECTS).
Lectured by Mario Boley

In this seminar we investigated the following questions:
*
How can we identify whether there exist any sub-populations in your data that stand out and are easy to describe?
How can we efficiently discover such subgroups from data? How can we do so with optimality guarantees?
How can we define 'standing out' in a meaningful manner? and, what does easy to describe mean?
*
We explored this from a local pattern mining, or better, subgroup discovery perspective.

## Don't Panic – or – How to Survive a PhD (SS'17)

**Special Lecture** (0 ECTS).
Lectured by Jilles Vreeken

Pursuing a PhD can be daunting, scary, stressful, and otherwise tricky business. With a bit of preparation, a few tips and tricks, and a dash of luck, pursuing a PhD can be awesome. In this talk I gave part of that preparation, and shared the tips and tricks I know of.

## Information Theory (WS'16)

**Seminar** (7 ECTS).
Lectured by Jilles Vreeken

We investigated the following questions:
*
What is structure, and what is noise?
What is a good model for data when we don't know what we're looking for?
What is the ultimate model for some given data, and how can we approximate that model in practice?
*
We explored these questions in light of Algorithmic Information Theory (AIT) and its practical variant, the Minimum Description Length (MDL) principle.

## Topics in Algorithmic Data Analysis (SS'16)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Pauli Miettinen

We investigated selected topics on algorithmic data analysis – which is more commonly known as *data mining*.
We looked into, amongst other topics,
pattern mining and association discovery,
measuring correlation and causation, and
and study mining complex data.

## Information Retrieval and Data Mining (WS'15)

**Core Lecture** (9 ECTS).
Lectured by Jilles Vreeken and Gerhard Weikum

Information Retrieval (IR) and Data Mining (DM) are methodologies for organizing, searching and analyzing digital contents. We looked into text indexing, query processing, search result ranking, and information extraction for semantic search, as well as pattern mining, clustering, classification and recommendation.

## Time Series Analytics (WS'15)

**Seminar** (7 ECTS).
Lectured by Hoang Vu Nguyen

In this seminar we studied advanced analysis methods for time series. We looked into
*
prediction, change detection, causal analysis, pattern discovery, and graph stream processing.
*

## Topics in Algorithmic Data Analysis (SS'15)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken

TADA SS'15 got an overall score of 1.25 – winning the **Busy Beaver award** for best CS lecture of SS'15!

We investigated selected topics on algorithmic data analysis – which is more commonly known as *data mining*.
We looked into
pattern mining and association discovery,
measuring correlation and causation, and
and study mining complex data.

## Information Theory (WS'14)

**Seminar** (7 ECTS).
Lectured by Jilles Vreeken

ITS WS'14 was evaluated with a 1.44 – making it the 3rd best seminar, and 7th best scoring CS lecture of WS'14!

We investigated the following questions:
*
What is structure, and what is noise?
What is a good model for data when we don't know what we're looking for?
What is the ultimate model for some given data, and how can we approximate that model in practice?
*
We explored these questions in light of Algorithmic Information Theory (AIT) and its practical variant, the Minimum Description Length (MDL) principle.

## Topics in Algorithmic Data Analysis (SS'14)

**Advanced Lecture** (6 ECTS).
Lectured by Jilles Vreeken and Pauli Miettinen

TADA SS'14 was evaluated with a 1.48 – making it the best scoring out of 27 Advanced CS lecture of SS'14!

We investigated selected topics on algorithmic data analysis, which is more commonly known as *data mining*.
We looked into
tensor factorisation methods in data mining,
information theoretic approaches to data analysis,
and study mining complex data.