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Summer 2020

Bachelor / Undergraduate

Projektgruppe

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 19A

Participants: Maximal 6

Beschreibung:

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

Wichtige Termine

  •  Vorbesprechung: t.b.a.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

ANNOUNCEMENT: If you take this course, please send an e-mail to [Email protection active, please enable JavaScript.] with subject ILASDM20.

Start date: 2020-04-17(delayed until further notice)

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:

   Fouad Alkhoury: Group I

   Kai Geißler: 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 (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

Important Dates

  • midterm exercise checkup: 2020-06-26
  • exam (1st try): t.b.a.
  • exam (2nd try): t.b.a. 

 

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

ANNOUNCEMENT: If you take this course, please send an e-mail to [Email protection active, please enable JavaScript.] with subject DSBD20.

Start date: 2020-04-15 (delayed until further notice)

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: Andel Gugu

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

Important Dates

  • midterm exercise checkup: 2019-06-24, 12-14, HS 7 (Endenicher Allee 19C)
  • exam (1st try): t.b.a.
  • exam (2nd try): t.b.a. 

Game AI
Master: MA-INF 4319

Study Period: Graduate / Hauptstudium

Lecturer: Prof. Dr. Christian Bauckhage

Contact: Prof. Dr. Christian Bauckhage

Details

Start date: 2020-04-06 (delayed until further notice)

Time:  Mon 10-12, Thu 10-12

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

Exercise time:  Mon 16-17.30

Exercise place: BIT Lecture Hall, Room 1.109, Endenicher Allee 19A

Course Materials

Seminars/Seminare

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

In this seminar we will focus on selected algorithms from the area of machine learning. In particular, we will read selected chapters of the book "Pattern Recognition and Machine Learning" by Christopher M. Bishop.

Study Period: Graduate / Hauptstudium

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

Contact: Eike Stadtländer

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  see below

Place: Endenicher Allee 19A

Participants: Max. 6

Course Materials

Important Dates

  • preliminary meeting: (delayed until further notice)2020-04-08 10:00-12:00 in Hörsaal 7 (CP1-HSZ)  (mandatory for participation). 

  • seminar meetings: 
      • 2020-04-20, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-05-11, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-05-18, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-05-25, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-06-08, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-06-22, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-07-06, 8.30h-10.00h, room 1.033, Informatik Zentrum, Endenicher Allee 19a
  • seminar writeup deadline: 2020-07-31 (23.59h)

Principles of Data Mining and Learning Algorithms:
Selected Papers from NeurIPS 2019
Master: MA-INF 4209

In this seminar we will focus on a selected few papers from last year's NeurIPS conference, which is one of the premier conferences in machine learning. The seminar consists of two sessions on how to give a talk at a conference, a discussion meeting, and the final presentation meeting, which will simulate a scientific conference with paper presentations.

Study Period: Graduate / Hauptstudium

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

Contact: Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  see below

Place: Endenicher Allee 19A

Participants: Max. 6

Course Materials

Important Dates

  • preliminary meeting: (delayed until further notice) 2020-04-08 10:00-12:00 in Hörsaal 7 (CP1-HSZ)  (mandatory for participation).

  • seminar meetings: 
      • 2020-04-22, 14.15-15.45h, presentation training 1, (mandatory), room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-04-29, 14.15-15.45h, presentation training 2, (mandatory), room 1.033, Informatik Zentrum, Endenicher Allee 19a
    • 2020-05-27 (tentative), discussion meeting (mandatory)
    • 2020-07-01, 14.15-17.45h, conference meeting, (mandatory), room 1.033, Informatik Zentrum, Endenicher Allee 19a
  • 2020-07-31 (23.59h) seminar writeup deadline

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

Contact: Dr. Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: Schloss Birlinghoven (Sankt Augustin)

Participants: Max. 8

Course Materials

Important Dates

  • preliminary meeting: (delayed until further notice) 2020-04-15 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:  2020-05-05 14.00h-16.00h room C2-024 at Fraunhofer Schloss Birlinghoven
  • 2nd presentation: 2020-05-26 14.00h-16.00h room C2-024 at Fraunhofer Schloss Birlinghoven
  • 3rd presentation: 2020-06-30 14.00h-16.00h room C2-024 at Fraunhofer Schloss Birlinghoven
  • lab report deadline: 2020-09-20
  • final presentation: 2020-09-29 room C2-024 14.00h-16.30h
 

Lab Development and Application of Data Mining and Learning Systems:
AI Language and Vision
Master: MA-INF 4306

In this lab, we will do cutting-edge Machine-Learning AI experiments related with what we look at (vision), what we speak (language), and something in between (knowledge). Topics include Visual Question-Answering, Imposing Structures onto Word-Embeddings, Ball-Embedding for ImageNet

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Christian Bauckhage, Dr. Tiansi Dong, Dr. Tamas Horvath, Dr. Pascal Welke

Contact: Dr. Tiansi Dong

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: Informatik Zentrum, Endenicher Allee 19A

Participants: Max. 8

Course Materials

Important Dates

  • preliminary meeting:  05. May 9:30h-11:30h (mandatory)
  • 1st presentation: 09. June 9:30h-11:30h (mandatory)
  • 2nd presentation: 06. July 9:30h-11:30h (mandatory)
  • final presentation: t.b.d. (mandatory)
  • lab report deadline: t.b.d. (preliminary version: one day before final presentation; final version: one week after final presentation)
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