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Winter 2020/2021

Bachelor / Undergraduate

Lectures/Vorlesungen

Datengetriebene Informatik
Bachelor BA-INF 035

Einführung in Datenbanken und Maschinelles Lernen

Lecturers: Prof. Dr. Christian Bauckhage

Contact: Dr. Pascal Welke

Details

Start date: 2020-10-26

Time:  Mo 14-16, Fr 14-16

Place: online

Exercise time:   tba

Exercise place: online

Tutors:

 tba

 

Beschreibung: 


Organisation & Materialien

Wichtige Termine

  • Bitte melden Sie sich bis zum 25.10.2020 im eCampus Kurs an. Links zu den Vorlesungen und Übungen werden dort zur Verfügung gestellt.
  • 1. Prüfung: tba
  • 2. Prüfung: tba

Projektgruppen

Wissensentdeckung und Maschinelles Lernen
Bachelor BA-INF 051

In dieser Projektgruppe werden grundlegende Algorithmen aus den Bereichen Wissensentdeckung und Data Mining erarbeitet, diskutiert, implementiert und empirisch evaluiert.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Florian Seiffarth, Till Schulz

Contact: Till Schulz

Supervisors: tba

Details

Time:     siehe unten

Place:   online

Participants: Maximal 6

Beschreibung:

In der Projektgruppe werden grundlegende Algorithmen aus den Bereichen Maschinelles Lernen, Wissensentdeckung und Data Mining 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

  •  Vorbesprechung:tba
  • 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.

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

Exercises: Eike Stadtländer

Contact: Dr. Tamas Horvath

Details

Start date: 2020-11-06

Time:  Fr 12:15-13:45 s.t.

Place: online

Exercise time:   Fr 14:00-15:30 s.t.

Exercise place: online

Tutors:

  Fouad Alkhoury

  Ani Karapetyan

 

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.

Course Organization & Materials

  • Materials are available via eCampus. You must enroll in eCampus on or before November 6 (Read how to access eCampus)
  • You are allowed to form excercise groups (of at least four and at most five 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 "ILAS2021: Excercise Groups" to Fouad Alkhoury, Ani Karapetyan, and Dr. Tamas Horvath with the name (surname, given name) and email addresses of all group members before 2020-11-05, 12:00 o'clock. Otherwise, you will be added to some group at random and informed about your group via email on 2020-11-05. Please check your emails on 2020-11-05 evening for your exercise class and tutor.

Important Dates

  • exam (1st try): tba
  • exam (2nd try): tba

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.

Lecturer: Dr. Tamas Horvath

Contact: Dr. Tamas Horvath

Details

Start date: 2020-11-04

Time:  Wed 14:15-15:45

Place: online

Exercise time:   Wed 16:00-17:30

Exercise place: online

Tutor: Richard Palme

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

  • Materials are available via eCampus. You must enroll in eCampus on or before 2020-11-04 (Read how to access eCampus)
  • You are allowed to form excercise groups (of at least four and at most five 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 "LNSD2021: Excercise Groups" to Richard Palme and Dr. Tamas Horvath with the name (surname, given name) and email addresses of all group members before 2020-11-03, 12:00 o'clock. Otherwise, you will be added to some group at random and informed about your group via email on 2020-11-03. Please check your emails on 2020-11-03 evening for your exercise class and tutor.

Important Dates

  • exam (1st try): tba
  • exam (2nd try): tba

Pattern Recognition (I)
Master: MA-INF 4229

Lecturer: Dr. Nico Piatkowski, Prof. Dr. Christian Bauckhage

Contact: Dr. Nico Piatkowski

Details

Start date: 

Time:  

Place:

Exercise time:  

Exercise place: 

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: 2020-10-27

Time:  Tue 16.30-18, Wed 9.30-11

Place: online

Exercise time:  Mon 16-17.30

Course Materials

Materials are available here

Seminars/Seminare

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

In this seminar we will focus on selected algorithms from the areas machine learning and data mining. In particular, we will read selected papers that were presented at this years ECML/PKDD conference, held in Ghent, Belgium.

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

Contact: Florian Seiffarth

Supervisors: tba

Prerequisites: MA-INF 4111 / MA-INF 4112 highly recommended

Details

Time:  see below

Place: online

Participants: Max. 6

Course Materials

Important Dates

  • preliminary meeting: first week of new semester, 26.10 - 30.10.2020, exact date tba. (mandatory for participation).

  • seminar meetings: tba.

    • seminar writeup deadline: tba.

     

    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.

    Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, PD Dr. Michael Mock, Dr. Pascal Welke

    Contact: Dr. Pascal Welke

    Supervisors: tba

    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 2020-10-26, 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: around. 10

    Course Materials

    Important Dates

    • preliminary meeting:  19.10.2020 14:00h-15:30h (mandatory)
    • 1st presentation: 17. 11.2020 14:00h-15:30h (mandatory)
    • 2nd presentation: 22. 12.2020 14:00h-15:30h (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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