Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghih Roohi, Dr. Azade Farshad, Yousef Yeganeh
Registration
- Registration must be done through TUM Matching Platform (please pay attention to the Deadlines)
- In order to increase your priority, please also apply via our own Registration system.
- The maximum number of participants: 24.
Announcements
- The preliminary meeting slides are available here.
- The preliminary meeting is scheduled for Feb 11st, from 15:00 to 15:30, with the following Zoom link:
https://tum-conf.zoom-x.de/j/62868796969?pwd=hsgvSjqSXCzPnDjSb35vaKMHpG0b6b.1
Introduction
- The aim of the course is to provide the students with notions about various machine learning techniques. The course is mainly defined by a project.
- The topics of the projects will be distributed at the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realized by couples.
Course Structure
- Presentation: 50% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
- Use the CAMP templates for PowerPoint camp-tum-jhu-slides-16_9.potx
- The guideline for mid-presentation can be found here: GuidelineMLMI_MidPresentation_SS2025.pdf
- The guidelines for the final presentation can be found here: GuidelineMLMI_Final presentation_SS2025.pdf
- Project Progress: 50% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository.)
Schedule
Date | Topic | Requirements | Description |
---|---|---|---|
16/06/2025 | Midterm Presentation |
| It is expected that the students are familiarized with the problem, and are able to discuss the aspects and possible solutions, have a clear roadmap, and have initial code |
04/08/2025 | Final Presentation |
| Students will briefly go through the problem statement, selected baselines, and discuss their results and analysis. |
08/09/2025 | Final Submission |
| Students work on their documentations (in Sharelatex.tum.de or Overleaf), finalize their missing experiments, and list their individual contributions. I.e., the report should contain the contributions each team member made to the project. |
Projects
Title | Tutors | Proposal | Students |
---|---|---|---|
Dehazing Echocardiography Images Using Generative Models | Shahrooz Faghihroohi | MLMI_Project_Summer2025_DehazingUS.pdf | Lara Lanz, Marc Fehlhaber, Leo Eckert, Selim Mert Kastan |
DMCAF: Diffusion Model Conditioning and Analytical Framework | MLMI_SS25_Diffusion Model Conditioning and Analytical Framework.pdf | Rayan Siala, Marco Busch, Umut Tulis, Burak Ayaz, Meric Feyzullahoglu | |
GDAF: Gene-Description Analytical Framework | MLMI_SS25_Gene-Description Analytical Framework.pdf | Jan Paul Grimm, XiangPeng Ye, Clemens Andre Grange, Daniel Platzner | |
GSCAF: Gaussian Splatting Conditioning and Analytical Framework | MLMI_SS25_Gaussian Splatting Conditioning and Analytical Framewor.pdf | David Bruns, Daniel Alexander Malik, Andreas Weiser, Clemens Schwarzmann |