Summer 2022

Bachelor / Undergraduate

Projektgruppe

Wissensentdeckung und Maschinelles Lernen
Bachelor BA-INF 051

In dieser Projektgruppe werden grundlegende Algorithmen aus dem Bereich Maschinelles Lernen erarbeitet, diskutiert, implementiert und empirisch evaluiert.

Study Period: Undergraduate / Grundstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Florian Seiffarth, Vanessa Toborek

Contact: Vanessa Toborek

Details

Time:     siehe unten

Place:    online

Participants: Maximal 6

In der Projektgruppe werden grundlegende Algorithmen aus dem Bereich Maschinelles Lernen vorgestellt. Die Aufgabe der Studierenden ist es, in Kleingruppen jeweils einen Algorithmus zu erarbeiten und einen wissenschaftlichen Vortrag darüber zu halten. Im Anschluss soll der Algorithmus implementiert und evaluiert werden. Neben einem Abschlussvortrag soll eine schriftliche Ausarbeitung erstellt werden.

Kursmaterialien

• Sind bei eCampus verfügbar.
  
• Wie man Zugriff zu eCampus erhält, erfahren Sie hier.

Wichtige Termine

• Die Vorbesprechung der Veranstaltungen Projektgruppe Wissensentdeckung und Maschinelles Lernen: Maschinelles Lernen und Projektgruppe Wissensentdeckung und Maschinelles Lernen: Wissensentdeckung werden gemeinsam stattfinden.
  
• Vorbesprechungstermin: 06.04.2022 10 Uhr
  
• Bitte registrieren Sie sich im ecampus Kurs bis zum 06.04.2022, um an der Vorbesprechung teilzunehmen. Wir werden dort einen Link und alle weiteren Informationen bereitstellen.
  

Master / Graduate

Lectures/Vorlesungen

Intelligent Learning and Analysis Systems: Data Mining and Knowledge Discovery
Master: MA-INF 4112

Introductory course to data mining and knowledge discovery.

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath

Contact: Dr. Tamas Horvath

Details

Start date: 2022-04-22

Time:  Fr 12:15-13:45 s.t.

Place:

• lecture on 2022-04-22 and 2022-04-29: online (Zoom link is available via eCampus)
• lectures from 2022-05-06 on-site: B-IT Lecture Hall, Room 0.109, Friedrich-Hirzebruch-Alle 6 (tentative; please follow the announcements)

Exercise time:  Fr 14:00-15:30 s.t.

Exercise place on-site: B-IT Lecture Hall, Room 0.109, Friedrich-Hirzebruch-Alle 6 (tentative; please follow the announcements)

Exercises:

• Richard Palme
  

Tutor:

• Johannes Linn

Description: 

With more and more data available for analysis and decision making - from web documents and digital media to sensory data from cameras, microphones, and ubiquitous devices - it becomes increasingly more important to understand how such large volumes of data can be analyzed by computers and used as the basis for new intelligent services, for decision making, and for making computers learn from experience. In companies around the world, from retail and banks all the way to Google, intelligent learning and analysis techniques are used to improve business decisions. Likewise, in science, important discoveries are made easier by automated learning methods, and games and other artifacts are being made adaptive with learning technology.

Within the intelligent systems track of the computer science Master's program, intelligent learning and analysis systems are one of the two major topics. This module (Data Mining) is one of the two modules that are offered as an introduction for master's students to Intelligent Learning and Analysis Systems. The other is the Machine Learning module taught in the winter semester. Both modules can be selected in either order, and you may choose to attend one or both of them. For a complete introduction to the topic, it is recommended to attend both modules.

In the Data Mining module in particular, we will focus more on the algorithms for discovering knowledge in large databases and on their technical properties such as scalability. We will get to know scalable variants of the decision tree methods that we have been looking at in the Machine Learning module, and discover algorithms for new Data Mining tasks that we have not been looking at there, in particular clustering, association rule discovery, subgroup discovery, discovery from spatial and geographic data, analysis algorithm for text and web documents, visualization options for data analysis. We will mostly be focusing on practical and algorithmic aspects which can be tried out with popular Data Mining packages, but will also have a chance to look at some of the theory behind the algorithms.

Course Organization & Materials

• Materials and Zoom links are available via eCampus. You must enroll in eCampus on or before April 21. (Read how to access eCampus)

Important Dates

exam

• 1st try: July 21, 2022, 12:00 - 14:00 in Friedrich-Hirzebruch Allee 5 - Hörsaal 1
• 2nd try: September 28, 2022, 11:00 - 13:00 in Friedrich-Hirzebruch Allee 5 - Hörsaal 1

Data Science and Big Data
Master: MA-INF 4212

Advanced course on big data analytics and systems.

Study Period: Graduate / Hauptstudium

Lecturer: Dr. Tamas Horvath, PD Dr. Michael Mock

Details

Start date: 2022-04-13

Time:  Wed 10:15-11:45 s.t.

Place:

• lectures in April (2022-04-13, 2022-04-20, and 2022-04-27): online (Zoom link is available via eCampus)
• lectures from 2022-05-04: on-site in CP1-HSZ / Hörsaal 7 (tentative; please follow the announcements)

Exercise time: Wed 12:00-13:30 s.t.

Exercise place:

• first exercise on 2022-04-27: online (Zoom link is available via eCampus)
• exercises from 2022-05-04: on-site in CP1-HSZ / Hörsaal 7 (tentative; please follow the announcements)

• Fouad Alkhoury

• Eike Stadtländer

The course offers an in-depth knowledge of different aspects of big data analytics and systems, including  algorithmic techniques for analyzing structured and unstructured data that cannot be stored in a single computer because it has enormous size and/or continuously arrives with such a high rate that requires immediate processing. In addition to the algorithmic aspects, distributed big data processing and database systems will be presented and applied.

Topics include similarity search,  synopses for massive data, mining massive graphs, classical data mining tasks for massive data and/or data streams, architectures and protocols for big data systems, distributed batch (Hadoop) and stream (Storm) processing systems, non-standard databases for big data (Cassandra).

Course Organization & Materials

• Materials and Zoom links are available via eCampus. You must enroll in eCampus on or before April 12. (Read how to access eCampus)

Important Dates

 exam
• 1st try: July 19, 2022, 10:00-12:00 in Friedrich-Hirzebruch Allee 5 - Hörsaal 1
• 2nd try: September 27, 10:00-12:00 in Friedrich-Hirzebruch Allee 5 - Hörsaal 3+4 and 5+6

Seminars/Seminare

Labs/Praktika

Lab Development and Application of Data Mining and Learning Systems: Machine Learning and Data Mining
Master: MA-INF 4306

In this lab, machine learning and data mining techniques are implemented and used in a wide range of applications.

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Till Schulz, Sebastian Müller

Contact: Sebastian Müller

Supervisors:  Till Schulz, Sebastian Müller

Prerequisites: MA-INF 4212 highly recommended

Details

Time: (see below)

Place: online (link on ecampus)

Participants: Max. 6

Course Materials

• Are available via eCampus.
• Read how to access eCampus
• LaTeX template for reports
  

Important Dates

• The mandatory preliminary meeting is on 07.04.2022 at 14:00h. Please register in the eCampus course is open until 06.04.2022 23:59h, to be able to attend the preliminary meeting. We will make a link to the meeting available on eCampus.