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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, Sebastian Müller

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: Online

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
 
 

Foundations of Quantum Computing
MA-INF 1107

Quantum computing harnesses quantum mechanical phenomena for information processing. For certain problems, this promises levels of efficiency unattainable on digital computers. Since prototypes of working quantum computers have now become available, it therefore seems reasonable to expect quantum computing to play an increasingly important role in the near future.

This lecture introduces the basic ideas and theoretical concepts behind quantum computing. To begin with, we study simple model systems from quantum mechanics to be able to appreciate "quantum weirdness". We also revisit Boolean algebras and Boolean functions to better understand how classical information processing differs from quantum information processing. We then study quantum bits (qubits), their properties, and the mathematics that describes their behavior. Finally, we study adiabatic quantum computing and its applications and conclude with a first look at quantum gate computing and its fundamentals.

 

Lecturer: Prof. Dr. Christian Bauckhage

Contact: Prof. Dr. Christian Bauckhage

Exercises: Patrick Seifner

Details

Start date: 2022-10-10

Time:  Mondays 10.15-12.45

Place: Hörsaal II, Meckenheimer Allee 176 

Exercise time: Mondays 14:15-15:45, every two weeks starting from 2022-10-24

Exercise place: B-IT Seminarraum 1.047

Course Materials

Check the eCampus page!

Important Dates

  • Don't forget the course registration deadline in BASIS, which is usually in November.
  • first exam: 6.2.2023 from 17:00 till 19:00; Place: HS 1 and 2 of Hörsaalzentrum Poppelsdorf (Friedrich-Hirzebruch-Allee 5) (Attention: NOT Meckenheimer Allee 176)
  • second exam: 27.3.2023 from 15:30 till 17:30; Place: HS 1 and 2 of Hörsaalzentrum Poppelsdorf (Friedrich-Hirzebruch-Allee 5) (Attention: NOT Meckenheimer Allee 176)
 
 
 
 

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 12.15-13.45

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
 
 

Advanced Methods For Text Mining

Besides being a collection of characters, text is up until now still considered to be one of the most commonly used medium of communication. The complex nature of textual data as well as their sheer amount though make building data mining models challenging. As part of the Master's Program in Media Informatics  at the Bonn-Aachen International Center for Information Technology (B-IT), this course will cover the state-of-the-art and established methods for mining textual datasets . The course will start with a lecture series on the preliminaries of data mining and machine learning, continue with a deep dive on advanced text mining methods involving Latent Semantic Indexing, Word Embeddings, conventional and resource efficient Recurrent Neural Networks for sequential representations from textual data, Attention Mechanism as well as Transformer architectures and end with lectures on applications from real wold involving natural language inference and information extraction in financial documents, recommender systems in the legal and audit domain and generative models for digital forensics.

Lecturer: Dr. Rafet Sifa

Contact: Dr. Rafet Sifa

Details

Start date: 2022-10-27

Time:  Thursday 14:00-15:30

Place: Hörsaal 0.109  (B-IT), Friedrich-Hirzebruch-Allee 6

Exercise time:  Thursday 12:15-13:45

Exercise place: Hörsaal 0.109  (B-IT), Friedrich-Hirzebruch-Allee 6

 

Course Materials

Basic knowledge of AI, data science, machine learning, and pattern recognition; programming skills; good working knowledge in statistics, linear algebra, and optimization. Having taken the related B-IT courses Mining Media Data I and Mining Media Data II is not a must. The course has 4-ETCS credits (with 2+2 SWS) and is taught by Dr. Rafet Sifa. The teaching assistant of the course will be Tobias Deußer. One week prior to each lecture a list of recommended readings (from text books, reports and research papers) related to the course work will be announced. For questions as well as the registration (see the news below) please write to [Email protection active, please enable JavaScript.].
 
Check https://sites.google.com/view/am4tm for news and updates.

Important Dates

  • 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, Vanessa Toborek, Eike Stadtländer

    Contact: Dr. Pascal Welke

    Details

    Schedule (tentative!): monthly on a Tuesday

    • Nov 08: Orientation
    • Nov 15: Literature Meeting
    • Nov 22: Presentation & Discussion
    • Dec 06: Presentation & Discussion
    • Dec 13: Presentation & Discussion
    • Jan 17: Presentation & Discussion

    Time: 14:15 s.t.

    Place: Room 3.110

    Participants: max. 8

    Course Materials

    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).

     

    Lab Development and Application of Data Mining and Learning Systems: Understanding and Applying Neural ODEs
    Master: MA-INF 4306

    In this lab we will study the Neural Ordinary Differential Equations (Neural ODE) approach (https://arxiv.org/abs/1806.07366) to the modelling of probability distributions that are define continuously in time. Specifically, we will model time series data as Markov Jump Processes (MJP).
    We will start by discussing the Neural ODE paper in some detail, as well as the basics of MJPs. Then we will use ideas from Generative Adversarial Networks (GAN) and Wasserstein Autoencoders (WAE) to fit MJPs, modelled via Neural ODEs, to time series data.

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

    Contact: Dr. Ramses Sanchez

    Supervisors: Dr. Ramses Sanchez

    Details

    Time: 6-week long Lab starting from February 13 until March 27

    Place: Tba

    Participants: Max. 6

    Further details: We plan to meet once a week, starting on Monday, February 13. We shall discuss the theory behind Neural ODEs and MJPs in the first two meetings. The other three meetings will be to discuss issues/questions you might have with either the coding or the math. Since we are doing only 6 weeks, we expect you to work full time on the lab.

    Course Materials

    Important Dates

    • First meeting, February 13, 10am, b-it room 3.110: Introduction to Neural ODEs

    • 2nd meeting, February 17, 10am, b-it room 3.110: Introduction to MJPs

    • Last meeting, March 27, 10am, b-it room 3.110: Final presentations/discussions
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