Practical Course:
Machine Learning in Medical Imaging
Prof. Nassir Navab, Dr. Shahrooz Faghih Roohi,
Dr. Seong Tae Kim,
Ashkan Khakzar,
Azade Farshad
Type: Master Practical Course
Module IN2106
SWS: 6
ECTS: 10 Credits
Location: TBA (Due to the pandemic situation we will have lectures online.)
Time: Thursdays, 16-18
Course Language: English
Announcements:
- 23-10-2020: Course schedule is announced in Moodle
- 16-07-2020: Please submit your application here. You also have to register via matching system. Using our application form is not mandatory, but it will help you get a higher ranking in the matching system from our side.
- 30-06-2020: Preliminary meeting: Thursday, 16.07.2020 (16:00-16:30) in virtual meeting room (zoom). Zoom access information is announced in Moodle
- 30-06-2020: Contact information-If you have any question for this seminar, please feel free to contact Dr. Seong Tae Kim (seongtae.kim@tum.de)
- 30-06-2020: Website is up!
About the Course
The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into
a lecture/excercises block and a
project.
- The lectures will include topics in image processing and analysis, unsupervised methods, and supervised methods. 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. The projects are to be realized by couples. The programming language for the projects is to be decided in agreement with your supervisor.
Registration
Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn't been reached (please pay attention to the Deadlines).
Maximum number of participants: 20.
Requirements
- Good knowledge of machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
- Strong knowledge of Python programming language
- Background in image processing
Evaluation
- 60% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository)
- 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
- Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.
Schedule
Please find lecture materials in Moodle
Projects
