Winter 2019/2020

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.

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

Contact: Till Schulz

Supervisors: Till Schulz & Florian Seiffarth

Details

Time:     siehe unten

Place:    Endenicher Allee 19A, CP1-HSZ / Hörsaal 4

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: 2019-10-09 15.00h (s.t.), CP1-HSZ / Hörsaal 4. 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: Machine Learning
Master: MA-INF 4111

Introductory course to machine learning.

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

Contact: Dr. Tamas Horvath

Details

Start date: 2019-10-25 (with two lectures)

Time:  Fr 12:15-13:45 s.t.

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

Exercise time:   Fr 14:00-15:30 s.t.

Exercise place:

  Group I: Hörsaal 3, Endenicher Allee 19C, Bonn

  Group II: Seminarraum 2.025, Endenicher Allee 19A, Bonn

  Group III: Hörsaal 4, Endenicher Allee 19C, Bonn

Tutors:

  Lukas Drexler (Group I)

  Nuria Storch de Gracia Fernandez (Group II)

  Pratika Kochar (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.

Course Organization & Materials

• Materials are available via eCampus. You must enroll in eCampus on or before November 9 (Read how to access eCampus)
• Form excercise groups (of three to four (3-4) students) and send an email with subject "ILAS1920: Exercise Groups" to all tutors with the name (surname, given name) and email addresses of all group members on or before November 6. If you need help forming a group, please inform us about it on or before October 31 via email to all tutors.
  

Important Dates

• midterm exercise checkup: 2020-01-24 (in the exercise time slot)
• exam (1st try): 2020-02-17, 12:00 - 14:00 in HS 3+4, 5+6, 7 (Endenicher Allee 19C, Bonn)
  
• exam (2nd try): The exam on March 16 has been CANCELLED! The new date will be announced via email and on this webpage.
  

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.

Lecturer: Dr. Tamas Horvath

Contact: Dr. Tamas Horvath

Details

Start date: 2019-10-16

Time:  Wed 14:15-15:45

Place: CP1-HSZ / Hörsaal 4

Exercise time:   Wed 16:00-17:30

Exercise place: CP1-HSZ / Hörsaal 4

Tutor: Maximilian Thiessen

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

• Materials are available via eCampus. You must enroll in eCampus on or before 2019-10-31 (Read how to access eCampus)

Important Dates

• exam (1st try): 2020-02-07, 11:00 - 13:00 in HS 3+4, 5+6, 7 (Endenicher Allee 19C, Bonn)
  
• exam (2nd try): 2020-03-09, 12:00 - 14:00 in HS 2 (Endenicher Allee 19C, Bonn)

Pattern Recognition (II)
Master: MA-INF 4229

Seminars/Seminare

Principles of Data Mining and Learning Algorithms:
Selected Papers from ECMLPKDD 2019
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 papers that were presented at this years ECML/PKDD conference, held in Würzburg, Germany.

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

Contact: Dr. Pascal Welke

Supervisors: Florian Seiffarth & Pascal Welke

Prerequisites: MA-INF 4111 / MA-INF 4112 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: 2019-10-09 14.00h (s.t.), CP1-HSZ / Hörsaal 4 (mandatory for participation).

• seminar meetings: t.b.a.

• seminar writeup deadline: t.b.a.

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.

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

Contact: Dr. Pascal Welke

Supervisors: Linara Adilova, Maram Akila, Sven Giesselbach, Michael Mock, Julia Rosenzweig, Dorina Weichert, 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: 2019-10-08 14.00h (s.t.) (room C2-104) 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: 5. November        14:00h-16:00h    mandatory progress meeting (tba)
  
• 2nd presentation: 10. December    14:00h-16:00h    mandatory progress meeting (tba)
• 3rd presentation: 7. January        14:00h-16:00h     mandatory progress meeting (tba)
• final presentation: 17. March        12:30h-16:00h    final lab presentation (tba)
• lab report deadline: 15. March/24.March 23.59h (preliminary version: one day before final presentation; final version: one week after final presentation)