Winter 2018/2019
Bachelor / Undergraduate
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, Pascal Welke,
Florian
Seiffarth, Till Schulz
Contact: Till Schulz
Details
Time: (see below)
Place: Endenicher Allee 19C - Hörsaal 4
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
- Werden bei eCampus verfügbar sein.
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Wie man Zugriff zu eCampus erhält, erfahren Sie hier.
Wichtige Termine
- Vorbesprechung: 2018-10-10 14:00-15:00 in Hörsaal 4. Eine Teilnahme an diesem Termin ist Voraussetzung für eine Teilnahme an der Projektgruppe.
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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.
Study Period: Graduate / Hauptstudium
Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath
Contact: Dr. Tamas Horvath
Details
Start date: 2018-10-19
Time: Friday 12:00-14:00 c.t. (12:15-13:45)
Place: Wegelerstr. 10 - Großer Hörsaal
Exercise time: Friday 14:00-16:00 c.t. (14:15-15:45)
Exercise place:
Group I: Endenicher Allee 19C - Hörsaal 3
Group II: Endenicher Allee 19C - Hörsaal 4
Tutors:
Lukas Drexler (Group I)
Tobias Elvermann (Group II)
Oliver Kiss (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.
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Read how to access eCampus.
Important Dates
- Start date: 2018-10-19
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midterm exercise checkup: 2019-01-11 (in the exercise slot)
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exam (1st try): 2019-02-12, 9:00-11:00 in HS 1 (Endenicher Allee 19C)
- exam (2nd try): 2019-03-14, 12:00-14:00 in HS 5+6, 7 (Endenicher Allee 19C)
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: 2018-10-17
Time: Wednesday 14:00-16:00 c.t. (14:15-15:45)
Place: Endenicher Allee 19C - Hörsaal 4
Exercise time: Wednesday 16:00 - 18:00 c.t. (16:15 - 17:45)
Exercise place: Endenicher Allee 19C - 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
- Will be available via eCampus.
-
Read how to access eCampus.
Important Dates
- Start date: 2018-10-17
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exam (1st try): 2019-02-05, 9:00-11:00 in HS 3+4 (Endenicher Allee 19C)
- exam (2nd try): 2019-03-07, 12:00-14:00 in HS 3+4 (Endenicher Allee 19C)
Seminars/Seminare
Principles of Data Mining and Learning Algorithms: Learning Theory
Master: MA-INF 4209
In this seminar we deal with theoretical aspects of algorithms in machine learning.
Study Period: Graduate / Hauptstudium
Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Pascal Welke, Michael Kamp
Contact: Pascal Welke
Prerequisites: MA-INF 4111 highly recommended
Details
Time: block seminar (see dates below)
Place: Endenicher Allee 19A
Participants: Max. 6
Description:
In this seminar we deal with theoretical aspects of algorithms in machine learning.
Course Materials
- Will be available via eCampus.
-
Read how to access eCampus.
Important Dates
- preliminary meeting: 2018-10-10 15:00-16:00 (Endenicher Allee 19C - Hörsaal 4) (mandatory for participation).
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meeting: 2018-10-17 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.067)
- meeting: 2018-10-31 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
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meeting: 2018-11-14 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
- meeting: 2018-11-28 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
- meeting: 2018-12-12 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
- meeting: 2019-01-09 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
- meeting: 2019-01-23 (Wed) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
Principles of Data Mining and Learning Algorithms: Selected Algorithms from Data Mining
Master: MA-INF 4209
In this seminar we will discuss different state-of-the-art data mining and machine learning algorithms.
Study Period: Graduate / Hauptstudium
Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Pascal Welke, Michael Kamp
Contact: Michael Kamp
Prerequisites: MA-INF 4212 highly recommended
Details
Time: block seminar (see dates below)
Place: Endenicher Allee 19A
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: 2018-10-10 15:00-16:00 (Endenicher Allee 19C - Hörsaal 4) (mandatory for participation).
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discussion meeting: 2018-11-09 (Fri) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
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presentations: 2018-12-18 (Tue) 10:00h-12:00h (Endenicher Allee 19A - room 1.033)
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report deadline: 2018-12-21 (Fri) 24:00h
Labs/Praktika
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, Linara Adilova, Birgit Kirsch, Nathalie Paul, Rajkumar Ramamurthy, Dorina Weichert, Pascal Welke
Contact: PD Dr. Michael Mock, Rajkumar Ramamurthy
Prerequisites: MA-INF 4212 highly recommended
Details
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
Important Dates
- preliminary meeting: 2018-10-09 14:00h-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: 2018-11-06 14:00h-16:00h (mandatory)
- progress meeting 2: 2018-12-04 14:00h-16:00h (mandatory)
- progress meeting 3: 2019-01-08 14:00h-16:00h (mandatory)
- progress meeting 4: 2019-02-05 14:00h-16:00h (optional)
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report draft deadline: 2019-03-05 12.00h (mandatory)
- final meeting: 2019-03-06 13:00h-16:00h (mandatory)
- lab report deadline: 2019-03-10 24.00h (mandatory)