Summer 2019

This is only a mirror of www.kdml.iai.uni-bonn.de/teaching. SS 2019 information should be checked there.

Bachelor / Undergraduate

Projektgruppe

Wissensentdeckung und Maschinelles Lernen: Data Mining
Bachelor BA-INF 051

In dieser Projektgruppe werden grundlegende Algorithmen aus den Bereichen Wissensentdeckung und Data Mining erarbeitet, diskutiert, implementiert und empirisch evaluiert.

Study Period: Undergraduate / Grundstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Florian Seiffarth, Till Schulz

Contact: Till Schulz

Details

Time:     siehe unten

Place:    Endenicher Allee 19C

Participants: Maximal 6

In der Projektgruppe werden grundlegende Algorithmen aus den Bereichen Maschinelles Lernen, Wissensentdeckung und Data Mining 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

•  Vorbesprechung: 2019-04-03 10:00-11:00 in Hörsaal 7 (CP1-HSZ) . Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.
• Erster Vortrag: t.b.a.
• Abschlussvortrag: t.b.a.
  
• Abschlussbericht: t.b.a.
  

Wissensentdeckung und Maschinelles Lernen: 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, Till Schulz

Contact: Florian Seiffarth

Details

Time:     siehe unten

Place:    Endenicher Allee 19C

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

•  Vorbesprechung: 2019-04-03 10:00-11:00 in Hörsaal 7 (CP1-HSZ) . Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.
• Erster Vortrag: t.b.a.
  
• Abschlussvortrag: t.b.a.
  
• Abschlussbericht: t.b.a.
  

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: 2019-04-12

Time:  Fr 12:15-13:45 s.t.

Place: B-IT Lecture Hall, Room 0.109, Endenicher Allee 19A

Exercise time:   Fr 14:00-15:30 s.t.

Exercise place:

  Group I: Hörsaal 3, Endenicher Allee 19C

  Group II: Seminarraum 2.025, Endenicher Allee 19A

  Group III: Hörsaal 4, Endenicher Allee 19C

Tutors:

  Lukas Drexler: Group I

  Tobias Elvermann: Group II

  Maximilian Thiessen: Group III

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 are available via eCampus. You must enroll in eCampus before 2019-04-26. (Read how to access eCampus)
• Form excercise groups (of three to four (3-4) students) and send an email with subject "ILAS19: Excercise Groups" to  Lukas Drexler and Maximilian Thiessen  with the name (surname, given name) and email addresses of all group members before 2019-04-17. If you need help forming a group, please inform us about it before 2019-04-17 via email to Lukas Drexler and Maximilian Thiessen.
  

Important Dates

• midterm exercise checkup: 2019-06-21
• exam (1st try): 2019-07-18, 15-17, HS 2 (Endenicher Allee19C)
• exam (2nd try): 2019-09-13, 9-11, HS 2 (Endenicher Allee 19C) 

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

Contact: Dr. Tamas Horvath

Details

Start date: 2019-04-10

Time:  Wed 10:15-11:45 s.t.

Place: CP1-HSZ / Hörsaal 7

Exercise time:   Mi 12:15-13:45 s.t.

Exercise place: CP1-HSZ / Hörsaal 7

Tutor: Tobias Elvermann

Description: 

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 Materials

• Materials are available via eCampus. You must enroll in eCampus before 2019-04-24. (Read how to access eCampus)

Important Dates

• midterm exercise checkup: 2019-06-19, 12-14, HS 7 (Endenicher Allee 19C)
  
• exam (1st try): 2019-07-15, 12-14, HS 2 (Endenicher Allee19C)
• exam (2nd try): 2019-09-10, 15-17, HS 1 (Endenicher Allee 19C) 

Pattern Recognition (I)
Master: MA-INF 4229

Seminars/Seminare

Principles of Data Mining and Learning Algorithms:
Selected Algorithms from Machine Learning and Data Mining
Master: MA-INF 4209

In this seminar we will focus on selected algorithms from the areas machine learning and data mining. In particular, we will read selected chapters of the book "Understanding Machine Learning: From Theory to Algorithms" by Shalev-Shwartz and Ben-David.

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Michael Kamp, Dr. Pascal Welke

Contact: Dr. Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  see below

Place: Endenicher Allee 19A

Participants: Max. 6

Course Materials

• Are available via eCampus.
• Read how to access eCampus.
  

Important Dates

• preliminary meeting: 2019-04-03 11:00-12:00 in Hörsaal 7 (CP1-HSZ)  (mandatory for participation).

• seminar meetings: 
• 2019-04-29, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
  
• 2019-05-06, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
• 2019-05-20, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
• 2019-06-03, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
• 2019-06-17, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
• 2019-07-01, 8.15h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a

• seminar writeup deadline: 2019-07-31
  

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, Michael Kamp, Dr. Pascal Welke

Contact: Dr. Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: Schloss Birlinghoven (Sankt Augustin)

Participants: Max. 8

Course Materials

• Are available via eCampus.
• Read how to access eCampus

Important Dates

• preliminary meeting: 2019-04-04 14.00h-16.00h, room C2-024 at Fraunhofer Schloss Birlinghoven. If you want to participate in this meeting, please send an email to [Email protection active, please enable JavaScript.] in advance.
  
• 1st presentation:  14. May 13.30h - 15.30h, room C4a-117 at Fraunhofer Schloss Birlinghoven
  
• 2nd presentation: 4. June 13.30h - 15.30h, room B3-318 at Fraunhofer Schloss Birlinghoven
  
• 3rd presentation: 2. July 13.30h - 15.00h, room C2-104 at Fraunhofer Schloss Birlinghoven
  
• final presentation: t.b.d.
  
• lab report deadline: t.b.d. (preliminary version: one day before final presentation; final version: one week after final presentation)