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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:    online

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

  • Bitte registrieren Sie sich im ecampus Kurs bis zum 20.04.2020, um an der Vorbesprechung teilzunehmen. Wir werden dort einen Link und alle weiteren Informationen bereitstellen.


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: Please register in the ecampus course until 2020-04-20. All teaching materials (including orga slides) will be available there.

Start date: 2020-04-24

Time:  Fr 12:15-13:45 s.t.

Place: online

Exercise time:  Fr 14:00-15:30 s.t.

Place: online

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

  • Materials are available via eCampus. Please enroll in eCampus before 2020-04-20 (see the announcement above). (Read how to access eCampus)
  • You are allowed to form excercise groups (of at least three and at most four students) if you know other students participating in this course. (We don't release the list of participants for privacy reasons.) In this case, please send an email with subject "ILAS20: Excercise Groups" to Fouad Alkhoury, Kai Geißler, Pratika Kochar, and Dr. Tamas Horvath with the name (surname, given name) and email addresses of all group members before 2020-04-29, 12:00 o'clock. Otherwise, you will be added to some group at random and informed about your group via email on 2020-04-29. Please check your emails on 2020-04-29 evening for your exercise class and tutor.

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: Please register in the ecampus course until 2020-04-20, to be able to attend the preliminary meeting.  All teaching materials (including orga slides) will be available there.

Start date: 2020-04-22

Time:  Wed 10:15-11:45 s.t.

Place: online

Exercise time:   Mi 12:15-13:45 s.t.

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
  • 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

Time:  Mon 10-12, Thu 10-12

Place: online

Exercise time:  Mon 16-17.30

Course Materials

 

Mining Media Data

Study Period: Graduate / Hauptstudium

Lecturer: Dr. Rafet Sifa and Prof. Dr. Christian Bauckhage

Contact: Dr. Rafet Sifa

Details

Start date: 2020-04-06

Time:  Tue 16.30-18, Wed 9.30-11

Place: online

Exercise time:  Mon 16-17.30

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: online

Participants: Max. 6

Course Materials

Important Dates

  • Please register in the ecampus course until 2020-04-20, to be able to attend the preliminary meeting. We will make a link available there.

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: online

Participants: Max. 6

Course Materials

Important Dates

  • Please register in the ecampus course until 2020-04-20, to be able to attend the preliminary meeting. We will make a link available there.

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

Supervisors: Linara Adilova, Dr. Ramses Sanchez, Till Schulz, Eike Stadtländer, Dorina Weichert, Dr. Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: online

Participants: Max. 8

Course Materials

Important Dates

  • Please register in the ecampus course until 2020-04-20, to be able to attend the preliminary meeting. We will make a link available there.
 

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: online

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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