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Summer 2018


Undergraduate/Grundstudium

Projektgruppe

Wissensentdeckung und Maschinelles Lernen
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,  Florian Seiffarth, Till Schulz

Contact: Till Schulz

Details

Time:     Blockveranstaltung (siehe unten)

Place:    Hörsaal 7 (CP1-HSZ)

Participants: Maximal 6

Beschreibung:

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

Wichtige Termine

  •  Vorbesprechung:  2018-04-11 11:00-12:00 in Hörsaal 7 (CP1-HSZ). Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.

 

Graduate/Hauptstudium

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: 2018-04-20

Time:  Fr 12:15-13:45 s.t.

Place:  MA176 / Hörsaal II

Exercise time:   Fr 14:00-15:30 s.t.

Exercise place:

  Group I: CP1-HSZ / Hörsaal 3

  Group II: CP1-HSZ / Hörsaal 4

  Group III: INF / B-IT / Seminarraum 2.025, Informatik III

Tutors:

  Annika Pick (Group I)

  Armin Schrenk (Group II)

  Sergey Kuptsov (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 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 Materials

Important Dates

  • midterm exercise checkup: 2018-06-29
  • exam (1st try): 2018-08-01, 9:00-11:00, HS 5+6, 7 (Endenicher Allee19C)
  • exam (2nd try): 2018-09-19, 12:00-14:00, HS 5+6, 7 (Endenicher Allee19C)

 

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: 2018-04-18

Time:  Mi 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: Eike Stadtländer

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: 2018-07-04
  • exam (1st try): 2018-07-31, 12-14, HS 1 (Endenicher Allee19C)
  • exam (2nd try): 2018-09-17, 12-14, HS 5+6, 7 (Endenicher Allee 19C) 


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 algorithmic aspects of big data analytics. We will focus on distributed learning.

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Michael Kamp, Florian Seiffarth, Till Schulz

Contact: Michael Kamp

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  seminar (see below)

Place: Schloss Birlinghoven (Sankt Augustin), room C1-214

Participants: Max. 6

Description: 

In this seminar we will discuss different state-of-the-art algorithms from data science and big data. We will focus on distributed learning.

Course Materials

Important Dates

  • preliminary meeting: 2018-04-09 14:00-15:00 (Schloss Birlinghoven, room C1-214) (mandatory for participation). Please send an email to Michael Kamp in advance if you want to participate in this meeting.

  • discussion meeting: 2018-05-07

  • presentations: 2018-06-26

  • seminar report deadline: 2018-07-10

 

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, Dr. Tamas Horvath, Michael Kamp

Contact: Michael Kamp

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: Schloss Birlinghoven (Sankt Augustin), room C1-214

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

Important Dates

  • preliminary meeting: 2018-04-09 13:00-14:00 (Schloss Birlinghoven, room C1-214)  (mandatory for participation). Please send an email to Michael Kamp in advance if you want to participate in this meeting.
  • presentations: t.b.a.
  • lab report deadline: t.b.a.
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