Universität Wien

250093 SE Applied Machine Learning (2025W)

4.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

First seminar is an organisational meeting: HS11 Wed Oct 1, 16:45.
During the rest of the semester, the seminar always takes place when presentations or meetings for supervision and discussion are planned and announced in advance.

  • Wednesday 01.10. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 08.10. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 15.10. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 22.10. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 29.10. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 05.11. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 12.11. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 19.11. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 26.11. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 03.12. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 10.12. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 17.12. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 07.01. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 14.01. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 21.01. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 28.01. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

This (project) seminar provides a forum for students to work out, present and discuss topics of "Applied Machine Learning" / "Mathematical AI/ML" / "Scientific Machine Learning" and an opportunity to deepen your knowledge obtained from previous machine learning courses.

Topics may cover, but are not limited to,
-) computational/numerical aspects, approaches and algorithms related to machine learning including, but not limited to, supervised and unsupervised learning methods, learning algorithms and computational optimization, reinforcement learning, extreme learning, deep learning, transfer learning, kernel methods, high-dimensional (parametric) (differential) equations, physics-informed and physics-aware machine learning, physics-informed neural networks, neural operators, neural differential equations, genetic algorithms, computer vision, evolution-based methods, automated knowledge acquisition, visualization of patterns in data, multi-strategy learning, multi-agent learning.
-) statistical modeling and engineering applications of machine learning / artificial intelligence including, but not limited to, data mining & big data, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, robotics and control, physics & astronomy, quantum methods in machine learning, bioinformatics, medicine, healthcare, biology, climate, education, business, finance and social sciences.
-) explainable AI (XAI) and trustworthy AI (TAI)

Topics freely chosen by the students can be pursued in consultation with the supervisor. Students are expected to conduct an independent literature search from text book chapter(s), scientific research publication(s), thesis/theses etc.
At least two of the following aspects should be covered: modeling, (numerical) analysis, numerical methods/algorithms, computer simulations, applications.
Each student develops a selected topic for an oral presentation and a written report, under the guidance of the professor but otherwise independently.

Assessment and permitted materials

classroom presentation (25+5 minutes) and a concise (1-page) written report.

Minimum requirements and assessment criteria

presentation + 1-page written workout.
Attendance required for the kick-off and your own presentation.

Examination topics

presentation + 1-page written workout

Reading list

depending on the chosen project, research paper(s), book chapter(s),...
Topic/paper suggestions will be provided.

Association in the course directory

MAMS; MSE; MEL

Last modified: Tu 21.10.2025 13:47