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250090 SE Applied Machine Learning (2025S)
Continuous assessment of course work
Labels
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).
- Registration is open from Sa 01.02.2025 00:00 to Su 23.02.2025 23:59
- Deregistration possible until Mo 31.03.2025 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
First seminar is an organisational meeting: HS11 Wed March 5, 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.
- N Wednesday 05.03. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 19.03. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 26.03. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 02.04. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 09.04. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 30.04. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 07.05. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 14.05. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 21.05. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 28.05. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 04.06. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 11.06. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 18.06. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
- Wednesday 25.06. 16:45 - 18:15 Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
classroom presentation (25+5 minutes) and a concise written report.
Minimum requirements and assessment criteria
presentation + workout
Examination topics
presentation + workout
Reading list
depending on the chosen project, research paper(s), book chapter(s),...
Association in the course directory
MAMS
Last modified: Mo 20.01.2025 09:46
-) 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, 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.