Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghih Roohi,  Ashkan Khakzar, Azade Farshad, Anees Kazi, Yusef Yegane

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: 20-24.

Announcements

• The preliminary meeting slides can be found here: MLMISS22.pdf
• The preliminary meeting is scheduled for Feb 3, 10:00 (Zoom link is visible on TUMonline in the course description).
• ُُThe lectures and presentations happen in hybrid form (the meeting info will be shared with participants via email).

Introduction

• The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture/exercises block and a project.
  • The lectures will include DL topics relevant to medical imaging applications. Each lecture will be followed by a practical hands-on exercise (e.g. the implementation in Python).
  • The topics of the projects will be distributed at the beginning of the semester. Each topic will be supervised by a different person.

Course Structure

In this Master Praktikum (Hauptseminar), students select one scientific article from the list provided by course organizers. The students read the paper, and must accomplish the following:

• 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.zip
  • The guideline for mid-presentation can be found here: Guideline for intermediate presentation (SS2022 MLMI).pdf
  • The guideline for the final presentation can be found here: GuidelineMLMI_Final presentation_SS2022.pdf
• Project Progress: 50% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository.

Schedule of Lectures 

Projects