Winter 2017/2018

Undergraduate/Grundstudium

Projektgruppe

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

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

Study Period: Undergraduate / Grundstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Krisztian Buza, Michael Kamp, Pascal Welke, Till Schulz

Contact:  Till Schulz

Details

Time:     Blockveranstaltung

Place:    AVZ III, Römerstr. 164

Participants: Maximal 6

In der Projektgruppe werden grundlegende Algorithmen aus den Bereichen Maschinelles Lernen, Wissensentdeckung und Data Mining vorgestellt. Die Aufgabe der Studenten ist es, in Kleingruppen sich 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

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

Wichtige Termine

•  Vorbesprechung:  2017-10-13 12.00-13.00h in HS1 (Römerstr. 164). Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.
• Erster Vortrag: 2017-12-06 14.00-17.00h 
• Abschlussvortrag: 2018-02-21 14.00-17.00h
• Abschlussbericht: 2018-02-28
  

Wissensentdeckung und Maschinelles Lernen: Machine Learning
Bachelor BA-INF 051

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

Study Period: Undergraduate / Grundstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Krisztian Buza, Michael Kamp, Pascal Welke, Till Schulz

Contact:  Till Schulz

Details

Time:     Blockveranstaltung

Place:    AVZ III, Römerstr. 164

Participants: Maximal 6

In der Projektgruppe werden grundlegende Algorithmen aus den Bereichen Maschinelles Lernen, Wissensentdeckung und Data Mining vorgestellt. Die Aufgabe der Studenten ist es, in Kleingruppen sich 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

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

Wichtige Termine

•  Vorbesprechung:  2017-10-13 12.00-13.00h in HS1 (Römerstr. 164). Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.
• Erster Vortrag: 2017-12-06 14.00-17.00h 
• Abschlussvortrag: 2018-02-21 14.00-17.00h
• Abschlussbericht: 2018-02-28
  

Graduate/Hauptstudium

Lectures/Vorlesungen

Intelligent Learning and Analysis Systems: Machine Learning
Master: MA-INF 4111

Introductory course to machine learning.

Study Period: Graduate / Hauptstudium

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

Contact: Dr. Tamas Horvath

Details

Start date: 2017-10-20

Time:  Friday 12:30-14:00 s.t.

Place:  HS 1 (Römerstr. 164)

Exercise time:   Friday 14:00-16:00 c.t. (14:15-15:45)

Exercise place:  A301 for group I, HS1 for group II; A207 for group III

Tutors:  Sergey Kupsov (group I), Joshua Chen (group II), Arsine Mangasaryan (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 (Machine Learning) 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 Data Mining module taught in the summer 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 Machine Learning module in particular, we will give a practically oriented introduction into the most popular methods from Machine Learning as a subfield of Intelligent Learning and Analysis Systems. We will get to know decision tree methods, instance-based learning, artificial neural networks, probabilistic learning, regression methods, kernel methods and support vector machines, and reinforcement learning for intelligent agents. This will be complemented with lectures on the most important approaches within computational learning theory. Within the exercises, it is possible to try out the most important methods and popular Machine Learning systems. 

Literature:

• Tom. M. Mitchell: Machine Learning.
• Peter Flach: Machine Learning: The Art and Science of Algorithms that Make Sense of Data.
• John D. Kelleher, Brian Mac Namee, Aoife D'Arcy: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. 

Course Materials

• Will be available via eCampus.
• Read how to access eCampus.
  

Important Dates

• Start date: 2017-10-20
• midterm exercise checkup: 2018-01-12 14:15-15:45h
• exam (1st try): 2018-02-16 9.00-11.00h (HS1 + HS2) (tentative)
  
• exam (2nd try): 2018-03-16 14.00-16.00h (HS1 + HS2) (tentative)

Learning from Non-Standard Data
Master: MA-INF 4303

This lecture introduces a selection of machine learning and data mining algorithms developed for graphs and relational structures.

Study Period: Graduate / Hauptstudium

Lecturer: Dr. Tamas Horvath

Contact: Dr. Tamas Horvath

Details

Start date: 2017-10-18

Time:  Wednesday 14:30-16:00 c.t.

Place: HS1 (Römerstr. 164)

Exercise time:   Wednesday 16.00 - 17.30 c.t.

Exercise place: A 121 (Römerstr. 164)

Tutor: Sergey Kuptsov

Description: 

Traditional machine learning and data mining algorithms are resorted to data that can be represented by a single table of fixed width; the rows and the columns correspond to objects and object attributes, respectively. This assumption turns out to be quite restrictive in numerous practical applications involving structured data, such as graphs or relational structures. The lecture will cover the basics of learning and mining graph structured data and relational structures. We will present various algorithms for this setting and analyse their computational properties. We will also discuss some interesting applications in bioinformatics, computational chemistry, and natural language processing.

Course Materials

• Will be available via eCampus.
• Read how to access eCampus.
  

Important Dates

• Start date: 2017-10-18
• exam (1st try): 2018-02-15 14.00-16.00h (HS 1) (tentative)
  
• exam (2nd try): 2018-03-13 14.00-16.00h (HS 1) (tentative)

Seminars/Seminare

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

In this seminar we will focus on local pattern mining.

Study Period: Graduate / Hauptstudium

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

Contact: Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Prelim. Meeting: 2017-10-13 13.00-14.00h (HS1, Römerstr. 164)

Time:  block seminar (see dates below)

Participants: Max. 6

Description: 

In this seminar we will discuss different state-of-the-art data mining and machine learning algorithms.

Course Materials

• Will be available via eCampus.
• Read how to access eCampus.
  

Important Dates

• preliminary meeting: 2017-10-13 13-14h (HS1, Römerstr. 164)(mandatory for participation)

• discussion meeting: 2017-11-14 14.00-16.00h (C1-214, Schloss Birlinghoven)

• presentations: 2017-12-19 14.00-16.00h (C1-214, Schloss Birlinghoven)

• report deadline: 2018-01-08 23.59h
  

Labs/Praktika

Lab Development and Application of Data Mining and Learning Systems: Data Science and Big Data
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, PD Dr. Michael Mock, Dr. Tamas Horvath, Dr. Krisztian Buza, Linara Adilova, Sven Giesselbach, Michael Kamp, Birgit Kirsch, Rajkumar Ramamurthy, Jil Sander, Klaudia Thellmann, Pascal Welke

Contact: Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Prelim. Meeting: 2017-10-17 14.00-16.00h (Schloss Birlinghoven, room B3-220)

Time:  (see dates below)

Place: Schloss Birlinghoven (Sankt Augustin)

Participants: Max. 8

Description:

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

Course Materials

• Will be available via eCampus.
• Read how to access eCampus.
  

Important Dates

• preliminary meeting: 2017-10-17 14.00-16.00h (Schloss Birlinghoven, room B3-220)  (mandatory for participation) Please send an email to Pascal Welke in advance if you want to participate in this meeting.
• progress meeting 1: 2017-11-07 15.00-17.00h (B3-220, Schloss Birlinghoven)
• progress meeting 2: 2017-12-05 15.00-17.00h (B3-318, Schloss Birlinghoven)
• progress meeting 3: 2018-01-09 15.00-17.00h (B3-318, Schloss Birlinghoven)
• final meeting: 2018-03-06 13.00-16.30h (C1-214, Schloss Birlinghoven)
• lab report deadline: 2018-03-12 23.59h