Current (SS'17)

Topics in Algorithmic Data Analysis

Advanced Lecture (6 ECTS) with lectures and assignments. Lectured by Jilles Vreeken

We will investigate selected topics on algorithmic data analysis – which is more commonly known as data mining. We will look into, amongst other topics, pattern mining and association discovery, measuring correlation and causation, and and study mining complex data. More information here.

Subgroup Discovery

Seminar (7 ECTS) with lectures, discussions, presentations. Lectured by Mario Boley

In this seminar we'll be investigating 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'll be exploring this from a local pattern mining, or better, subgroup discovery perspective. More information here.


Upcoming (WS'17/18)

Information Retrieval and Data Mining (WS'17/18)

Core Lecture (9 ECTS) with lectures and assignments. 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 will particularly look 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 will likely cover include text indexing, search result ranking, and information extraction for semantic search, as well as pattern mining, clustering, classification and recommendation. More information soon.


Recent

Don't Panic – or, How to Survive your PhD (SS'17)

Single 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 aim to give you that preparation, and share the tips and tricks I know of. More information here.

The Information Theory Seminar (WS'16)

Seminar (7 ECTS) with lectures, discussions, presentations. 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. More information here.

Topics in Algorithmic Data Analysis (SS'16)

Advanced Lecture (6 ECTS) with lectures and assignments. 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. More information here.

Information Retrieval and Data Mining (WS'15)

Core Lecture (9 ECTS) with lectures and assignments. Lectured by Gerhard Weikum and Jilles Vreeken.

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. More information here.

Time Series Analytics (WS'15)

Seminar (7 ECTS) with lectures, discussions, presentations. Lectured by Hoang Vu Nguyen and Jilles Vreeken.

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. More information here.

Topics in Algorithmic Data Analysis (SS'15)

Advanced Lecture (5 ECTS) with lectures and assignments. 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. More information here.

The Information Theory Seminar (WS'14)

Seminar (7 ECTS) with lectures, discussions, presentations. 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. More information here.

Topics in Algorithmic Data Analysis (SS'14)

Advanced Lecture (5 ECTS) with lectures and assignments. Lectured by Pauli Miettinen and Jilles Vreeken.

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. More information here.