Prof. Nassir Navab
Machine Learning in Medical Imaging
, Dr. Shadi Albarqouni
, Christoph Baur
, Magda Paschali
, Huseyin Coskun
, Abhijit Guha-Roy
, Roger Soberanis
, Anjany Sekuboyina
Master Practical Course Module IN2106
CAMP Seminar Room, 03.13.010
- 20-10-2018: Projects are assigned!
- 28-09-2018: Lectures starts at 12 o'clock!
- 28-09-2018: Grading is updated! 60% Project Progress (Done by your tutor -- mainly on your weakly progress on lrz git repository), and 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentations skill, progress so far comapred to other groups ...etc.)
- 28-09-2018: Projects will be announced soon (second week of October)
- 29-06-2018: Registration, through TUM Matching Platform, should be done between 29.06. to 04.07.18
- 19-06-2018: A tentative schedule is up!
- 16-06-2018: An introductory meeting: Wednesday, 27.06.2018 (12:00-12:30) in CAMP Seminar Room, 03.13.010.
- 16-06-2018: 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. 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.
- Interested students should attend the introductory meeting to enlist in the course.
- 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).
- A maximum number of participants: 20.
- 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
- Basic knowledge of C++ is advantageous (in case you decide to use c++ for your final project): knowledge of main concepts of object-oriented programming, basic software engineering, image processing libraries and linear algebra libraries.
- 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.