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Sommer 2016


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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

Max. 6 participants

Bei Interesse an der Projektgruppe melden Sie sich bitte bei Katrin Ullrich. Wir nehmen weiterhin Anmeldungen zu dieser Veranstaltung an.

Details

Prelim. Meeting: 2016-04-15, 14:00-15:00, room AVZ III / A 207
Time:     Blockveranstaltung

You can access the materials to this course in eCampus

  • Dates: tba
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.

Ankündigung(en)

Die Vorbesprechung wird am 2016-04-15 um 14:00-15:00 in Raum AVZ III / A 207AVZ III / A207 (Römerstr. 164) stattfinden.



Graduate/Hauptstudium

Vorlesungen

Intelligent Learning and Analysis Systems: Data Mining and Knowledge Discovery
Master: MA-INF 4112

Introductory course to machine learning.

Study Period: Graduate / Hauptstudium

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

Contact: Dr. Tamas Horvath

Details

Start date: 2016-04-22
Time:  Fr 12:30-14:00 s.t.
Place:
AVZ III / 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:  

 You can access the materials to this course in eCampus

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.

 

Announcements

Important Dates

  • midterm exercise checkup: 2016-07-01, in the exercise slot
  • exam 1st try: 2016-08-05, 14:00-16:00 in HS 1+2 (Römerstr. 164)
  • exam 2nd try: 2016-09-29, 14:00-16:00 in HS 1+2 (Römerstr. 164)

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: 2016-04-13
Time:  Wed 14.30-16.00 s.t.
Place:
AVZ III / A207
Exercise time:  
Wed 16:00-17.30
Exercise place:
AVZ III / A207 and AVZ III / A121
Tutors:
Alvin Tjondrowiguno 

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).

recommended prerequisites:
   MA-INF 4111 – Intelligent Learning and Analysis Systems: Machine Learning
   MA-INF 4112 – Intelligent Learning and Analysis Systems: Data Mining and Knowledge Discovery

You can access the materials to this course in eCampus

Announcements

 

Important Dates

    • midterm exercise checkup: 2016-06-22, in the exercise slot
    • exam 1st try: 2016-07-28, 9:00-11:00 in HS 1 (Römerstr. 164)
    • exam 2nd try: 2016-09-30, 9:00-11:00 in HS 1 (Römerstr. 164)

    Seminar

    Principles of Data Mining and Learning Algorithms:
    Mining and Learning from Graph Structured Data
    Master: MA-INF 4209

    In this seminar we will discuss different state-of-the-art algorithms for graph mining or learning from graph structured data.

    Study Period: Graduate / Hauptstudium

    Lecturers: Prof. Dr. Stefan Wrobel, Pascal Welke

    Contact: Pascal Welke

    Prerequisites: MA-INF 4111 or 4112 or 4303 highly recommended

    Max. 8 participants

    Preliminary Meeting: 2016-04-15, 13:00-14:00, room AVZ III / HS 1

    Details

    Prelim. Meeting: 2016-04-15 (Fr), 13:00-14:00, room AVZ III / HS 1

    Time:  block seminar

    • Dates tba

    Place: Schloss Birlinghoven (Sankt Augustin)

    Description: 

    In this seminar we will discuss different state-of-the-art algorithms for graph mining or learning from graph structured data.

    You can access the materials to this course in eCampus

    Announcements

     

    Important Dates

    • preliminary meeting: 2016-04-15, 13:00-14:00, room AVZ III / HS 1
    • 2016-06-14,  14:00-16:00. room IZB C1-214
    • 2016-06-16,  14:00-16:00. room IZB C1-220
    • 2016-06-21,  14:00-16:00. room IZB C1-214
    • 2016-06-23,  14:00-16:00. room IZB C1-214


    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 4112 highly recommended

    Max. 8 participants

    Preliminary Meeting: 2016-04-12, 14:00-16:00 in Schloss Birlinghoven, Sankt Augustin, in room B3-318

    Important: if you are going to participate in the preliminary meeting, please send an e-mail to Katrin Ullrich

    Details

    Prelim. Meeting: 2016-04-12, 14:00-16:00 in Schloss Birlinghoven, Sankt Augustin, in room B3-318

    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.

    You can access the materials to this course in eCampus

    Announcements

    • The preliminary meeting will take place on 2016-04-12 at 14:00-16:00 in Schloss Birlinghoven, Sankt Augustin, in room B3-318

    Important Dates

    Prelim. Meeting: 2016-04-12, 14:00-16:00 in Schloss Birlinghoven, Sankt Augustin, in room B3-318

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