Winter 2015/2016

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

Contact: Katrin Ullrich

Details

Prelim. Meeting:  October 23, 2015 (Fr), 2:15-3:15 p.m., room AVZ III / A207
Time:     Blockveranstaltung
Place:    Schloß Birlinghoven, Sankt Augustin

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.

Ankündigung(en)

Die Vorbesprechung wird am 23.10.2015 (Fr) von 14:15 bis 15:15 Uhr in Raum AVZ III / A207 (Römerstr. 164) stattfinden.

Die Materialien für diese Veranstaltung können hier heruntergeladen werden.

Graduate/Hauptstudium

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: 2015-10-30
Time:  Fr 12:30-14:00 s.t.
Place: HS 1
Exercise time:   Fr 14:00-16:00 c.t. (14:15-15:45)
Exercise place: AVZ III / A207 for group I; AVZ III / A301 for group II, AVZ III / HS1 for group III

Tutors:  

• Linara Adilova (Group I, A207)
• Lukasz Segiet (Group II, A301)
  
• Alvin Tjondrowiguno (Group III, HS1)

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. 

Announcements

You can access and download Exercise Sheets and Lecture Slides here.

Important Dates

• midterm: January 22, 2016, in the exercise slot
• exam (1st try): February 19, 2016, 13:00-15:00, in HS 1+2 AVZIII
• exam (2nd try): March 16, 2016, 10:00-12:00, in HS 1+2 AVZIII

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: 2015-10-26
Time:  Mo 12:45-14:15 s.t.
Place: AVZ III / A207
Exercise time:   Mo 14:30-16:00
Exercise place: AVZ III / A207
Tutor: Ivan Danielov Ivanov 

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.

Announcements

You can access and download Exercise Sheets and Lecture Slides here.

Important Dates

• exam (1st try): February 16, 2016, 13:00-15:00, in HS 1+2 AVZIII
• exam (2nd try): March 18, 2016, 10:00-12:00, in HS 1+2 AVZIII 

Seminar

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

In this seminar we will focus on algorithmic aspects of big data analytics. 

Study Period: Graduate / Hauptstudium

Lecturers: Prof. Dr. Stefan Wrobel, Pascal Welke

Contact: Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Prelim. Meeting: October 23, 2015 (Fr), 1:00-2:00 p.m., room AVZ III / A207

Time:  block seminar

• Jan 26 (Tue) 14:00-17:00
• Jan 27 (Wed) 14:00-17:00
  

Place: Schloss Birlinghoven (Sankt Augustin)

Description: 

In this seminar we will discuss different state-of-the-art algorithms from data science and big data.

Announcements

Important Dates

• first block of presentations: Jan 26 (Tue) 2-5pm
• second block of presentations: Jan 27 (Wed) 2-5pm
• final reports: Feb 6
  

Praktikum

Lab Development and Application of Data Mining and Learning Systems
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, Katrin Ullrich

Contact: Katrin Ullrich

Prerequisites: MA-INF 4111 or MA-INF 4212 highly recommended

Details

Prelim. Meeting: October 20, 2015 (Tue), 2:30 - 3:30pm
Time:  first Tuesday of every month
Place: Schloss Birlinghoven (Sankt Augustin)
Description:

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

Announcements

• The preliminary meeting will take place on Tuesday, October 20, 2015 at 2:30–3:30 p.m. in room C1-214 at Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin
  
• Materials and course slides can be accessed here

Important Dates

• February 2, 2:30-4:30pm, Room B3-318: lab progress presentation (optional)
• March 1, 2:30-4:30pm, Room B3-318: final presentation