Wintersemester 2023/2024

Bachelor

Projektgruppen

Wissensentdeckung und Maschinelles Lernen: Research Projects

Bachelor: BA-INF 051

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.

Details

Preliminary Meeting

Mittwoch, 11. Oktober 2023
11 Uhr (s.t.)

Institut für Informatik Raum 3.110

Participants

max. 6

Prerequisites

none

Registration

Please register in ecampus for the course until 10.10.2023 , see the button below.

(Computer) Science Communication

Bachelor: BA-INF 051

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.

Details

Preliminary Meeting

Mittwoch, 11. Oktober 2023
11 Uhr (s.t.)

Institut für Informatik Raum 3.110

Participants

max. 6

Prerequisites

none

Registration

Please register in ecampus for the course until 10.10.2023 , see the button below.

Master

Lectures

Algorithms for Data Science

Master MA-INF 4112

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, the Algorithms for Data Science course offers 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 particular, the topics include classical data mining tasks for massive data and/or data streams, mining massive graphs, and similarity search in massive data.

 

Lecturers

Exercises

Details

Lecture - Start/Time/Place

18. October 2023
Wednesdays, 14:00 Uhr - 16:00 Uhr (c.t.)

Exercises - Start/Time/Place

25. October 2023
Wednesday, 16:00 Uhr - 18:00 Uhr (s.t.)

CP1-HSZ, Hörsaal IV

Room 1.047, Friedrich-Hirzebruch-Allee 8

Tutors

Prerequisites

none

Registration

Please register in ecampus on or before 19.10.2023. (Read how to access eCampus)

Important Dates

Exam (1st try)

15. February 2024

Exam (2nd try)

14. March 2024

Principles of Machine Learning

Master MA-INF 4111

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.

Details

Lecture - Start/Time/Place

16. October 2023
Mondays, 10:00 Uhr - 12:00 Uhr (c.t.)

CP1-HSZ, Hörsaal IV

Exercises - Start/Time/Place

16. October 2023
Mondays, 12:00 Uhr - 14:00 Uhr (c.t.), every two weeks

Place: tba

Tutors

Prerequisites

none

Registration

Please register in ecampus on or before 16.10.2023. (Read how to access eCampus)

Important Dates

Exam (1st try)

13.02.2024, 10:00 - 12:00, HS 1+2 Hörsaalzentrum Poppelsdorf

Exam (2nd try)

28.03.2024, 12:00 - 14:00, HS 1 Hörsaalzentrum Poppelsdorf

Seminars

Principles of Data Mining and Learning Algorithms: Selected Papers from State-of-the-Art

Master: MA-INF 4209

The seminar will held as a block seminar. Further seminar dates will be announced during the preliminary meeting.

Details

Preliminary Meeting

Wednesday, October 18, 2023
10am (c.t.)

Room 3.111 (tentative!)

Participants

max. 12

Prerequisites

none

Registration

Please register in eCampus for the course until 17.10.2023, see the button below.

Principles of Data Mining and Learning Algorithms: Selected Papers from ECML PKDD 2023

Master: MA-INF 4306

In this seminar, we will simulate the experience of a scientific conference. It will consist of two or three sessions, each dedicated to a recent topic of research interest in the realm of machine learning. For each session, three papers from this year's ECML PKDD conference, one of Europe's premier machine learning conferences, will be the focal point. Students participating in a session are required to read all three papers relevant to their topic. Subsequently, each student will be assigned one specific paper to present during the simulated conference meeting. This approach ensures a deep dive into the chosen topic while fostering skills in research comprehension and presentation.

Details

Preliminary Meeting

Tuesday, October 17, 2023
2pm

location t.b.a.

Participants

max. 6

Prerequisites

none (MA-INF 4111 recommended)

Registration

Please register in ecampus for the course until 16.10.2023 , see the button below.

Seminar Principles of Data Mining and Learning Algorithms: On Neural Operators

Master: MA-INF 4209

In this seminar we will study the concept of neural operators, which employ neural networks to learn mappings between infinite dimensional spaces. In practice this means one can train neural networks to e.g. automatically solve entire families of complex partial differential equations, integrate families of functions or infer families of stochastic processes.

Our main goals will be to:

  • test these ideas with some simple coding exercises we will do from scratch (In fact, we will empirically demonstrate we can teach a neural network to integrate "any" "nice" function on the real line)
  • read and discuss about some current problems of neural operators and their possible solutions
  • if we have time we might discuss how to apply the ideas of neural operators to concepts of representation theory

What you will have to do: we will ask you to give two short presentations about some numerical experiments, and hand in a final report

Dates:

  • the seminar will run from Monday, February 19, 2024, until March 31 (6 weeks in total).
  • Registration (and deregistration) deadline is on: Friday, March 15, 2024
  • Preliminary meeting: Monday, February 19, 2024 at 10am in Room 3.110 B-IT

Contact

Details

Preliminary Meeting

Monday, February 19, 2024

10 am

Room 3.110 B-IT

Participants

max. 6

Prerequisites

none

Registration

Please register in Basis after our Preliminary Meeting.

Registration deadline: March 15, 2024

Labs

Development and Application of Data Mining and Learning Systems: 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. The preliminary meeting in mandatory. If you are interested in this lab, join us for the first sessions where we will present this semester's topics and discuss all organisational matters.

Contact

Details

Preliminary Meeting

Wednesday, 04. October 2023
2 PM s.t.

Participants

max. 6

Prerequisites

none

Registration

Please register in eCampus for the course until 16.10.2023 , see the button below.

Development and Application of Data Mining and Learning Systems: Data Science

Master: MA-INF 4306

In this lab, machine learning and data mining techniques are implemented and used in a wide range of applications. The preliminary meeting in mandatory. If you are interested in this lab, join us for the first sessions where we will present this semester's topics and discuss all organisational matters.

Details

Preliminary Meeting

Wednesday, 04. October 2023
2 PM s.t.

Participants

max. 6

Prerequisites

none

Registration

Please register in eCampus for the course until 16.10.2023 , see the button below.

Wird geladen