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Winter 2022/2023

Bachelor / Undergraduate

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, Vanessa Toborek

Contact:  Vanessa Toborek

Details

Time:    Vorbesprechung am 12.10.2022 14:00-16:00

Place:   Friedrich-Hirzebruch Allee 5 - Hörsaal 4

Participants: Maximal 6

Beschreibung:

In den Projektgruppen 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: 12.10.2022 um 14:00-16:00 in Hörsaal 4.
  • Bitte registrieren Sie sich im ecampus Kurs bis zum 11.10.2022, um an der Vorbesprechung teilzunehmen. Wir werden dort alle weiteren Informationen bereitstellen.
 
 
 

(Computer) Science Communication
Bachelor BA-INF 051

Diese Projektgruppe wird erstmalig angeboten. Auch hier werden grundlegende Algorithmen aus den Bereichen Wissensentdeckung und Data Mining erarbeitet und diskutiert. Im Mittelpunkt steht allerdings die Erarbeitung der Inhalte für verschiedene Zielgruppen. Die Aufgabe der Studierenden ist die Vermittlung von Wissen in Form eines Coding Nuggets als praktische Anleitung für Fachleute als auch in Form eines Blog Beitrags für interessierte Laien.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Florian Seiffarth, Vanessa Toborek

Contact:  Vanessa Toborek

Details

Time:    Vorbesprechung am 12.10.2022 14:00-16:00

Place:   Friedrich-Hirzebruch Allee 5 - Hörsaal 4

Participants: Maximal 6

Beschreibung:

In den Projektgruppen 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 wird eine schriftliche Ausarbeitung in zweierlei Form für zwei unterschiedliche Zielgruppen erstellt: Fachleute und interessierte Laien. Dabei wird auf die unterschiedlichen Anforderungen bei der Wissenvermittlung für unterschiedliche Zielgruppen eingegangen.

Kursmaterialien

Wichtige Termine

  • Vorbesprechung: 12.10.2022 um 14:00-16:00 in Hörsaal 4.
  • Bitte registrieren Sie sich im ecampus Kurs bis zum 11.10.2022, um an der Vorbesprechung teilzunehmen. Wir werden dort alle weiteren Informationen bereitstellen.
 
 

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: 2022-10-21

Time:  Fr 12:15-13:45

Place: B-IT Lecture Hall, Room 0.109, Friedrich-Hirzebruch-Alle 6

Exercise time:   Fr 14:00-15:30

Exercise place: B-IT Lecture Hall, Room 0.109, Friedrich-Hirzebruch-Alle 6

 

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 and Zoom links are available via eCampus. You must enroll in eCampus on or before October 21. (Read how to access eCampus)

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

Exercises: Fouad Alkhoury

Contact: Dr. Tamas Horvath

Details

Start date: 2022-10-19

Time:  Wed 14:15-15:45

Place: CP1-HSZ / Hörsaal 4

Exercise time:   Wed 16:00-17:30

Exercise place: CP1-HSZ / Hörsaal 4

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 Organization & Materials

Materials and Zoom links are available via eCampus. You must enroll in eCampus on or before October 19. (Read how to access eCampus)

Important Dates

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

Graph Representation Learning
MA-INF 4316

We will discuss general approaches for machine learning (ML) on graph structured data. In particular, computational methods for graph representation learning such as graph neural networks (GNNs), graph kernels, as well as graph mining techniques will be discussed, analyzed, and applied. Regarding GNNs and graph kernels, we will discuss the expressive power and how these concepts are related, as well as several specific examples. In the area of graph mining, we will likely investigate fast (approximate) algorithms to count small patterns, such as triangles, or trees. If time permits, we might venture into the realm of ranking on large-scale graphs, with applications such as recommender systems. The exercises will focus on practical implementations and the application of these methods to real world examples.

Lecturer: Dr. Pascal Welke

Contact: Dr. Pascal Welke

Exercises: Till Schulz

Tutor: Lukas Conrads

Details

Start date: 2022-10-10

Time:  Mondays 14.15-15.45

Place: Hybrid (HS 5+6, Hörsaalzentrum Poppelsdorf, Friedrich Hirzebruch Allee 5)

Exercise time:  Tuesdays 16.15-17.45

Exercise place: (INF / B-IT / Seminarraum 1.047, Informatik III, Friedrich Hirzebruch Allee 8)

Course Materials

I am planning to give this lecture in a hybrid fashion: I will be present in the lecture hall and will stream the presentation and audio live via zoom. You may join physically or virtually.

Materials are available via eCampus. You should enroll in eCampus on or before 2022-10-10 to get access to the lecture materials, such as zoom links, slides and exercise sheets.

Important Dates

  • Don't forget the course registration deadline in BASIS, which is usually in November.
  • first exam: tba
  • second exam: tba
 

Seminars/Seminare

Principles of Data Mining and Learning Algorithms - Selected Papers from ML Conference
Master: MA-INF 4209

In this seminar we will focus on a selected few papers from the NeurIPS conference, which is one of the premier conferences in machine learning. The seminar consists of a discussion meeting, and one final presentation meeting, which will simulate a scientific conference with paper presentations.

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

Contact: Fouad Alkhoury

Details

Time:  (see below)

Place: online

Participants: Max. 6

Course Materials

Important Dates

  • preliminary meeting: 2022-10-18 10:00h - 11:00h (online)

  • Please register in the ecampus course until 2022-10-17. We will make a link to the meeting available on ecampus.

 
 
     

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

    Ethics is an increasingly important topic in all areas of AI. It raises many fundamental questions about both the researchers/developers implementing AI systems (e.g., "how to collect training and test data without violating anyone's privacy?) as well as the AI systems as moral agents themselves (see, e.g., the famous trolley problem for autonomous vehicles). In this seminar, you take on a role in an (imaginary) committee whose objective it is to constitute a new MSc. in Artificial Intelligence. Your specific task is to decide if ethics should be integrated into the programme and if so what should be taught and how should it be taught. For that purpose, we will read and discuss official guidelines, research papers, and book excerpts about ethics in artificial intelligence.

    Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Sebastian Müller, Eike Stadtländer

    Contact: Dr. Pascal Welke

    Details

    Schedule (tentative!): monthly on a Tuesday

    • 2022-11-01
    • 2022-12-06
    • 2023-01-10
    • 2023-02-07

    Time: 14:15 s.t.

    Place: Room 3.110

    Participants: ca. 9

    Course Materials

    Materials are available via eCampus.

    Important Dates

    • preliminary meeting: 2022-10-18 14:00h - 15:00h - Room 3.110

     

    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: Sebastian Müller

    Supervisors: Michael Mock, Sebastian Müller

    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 2022-10-18.

    • Preliminary Meeting: 2022-10-19 14.00h - 16.00h (online).

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