Prof. Nassir Navab
Machine Learning in Medical Imaging
, Shadi Albarqouni
, Christoph Baur
, Mai Bui
, Huseyin Coskun
, Abhijit Guha-Roy
, Roger Soberanis
Master Practical Course Module IN2106
CAMP Seminar Room, 03.13.010
- 10-07-2017: Please register via TUM Matching System within 14.07.2017 - 19.07.2017 .
- 26-06-2017: An introductory meeting: Monday, 10.07.2017 (16:15-16:45) in CAMP Seminar Room, 03.13.010.
- 26-06-2017: 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 in 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 in 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: 18.
- Strong knowledge of Python, MATLAB or another programming language
- Background in image processing
- Exercises will be held in preferably in Python, although an introductory lecture on Python will be given previous knowledge would be advantageous.
- 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.
- Basic knowledge of naive machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
The grade will be split between weekly exercises associated to each lecture and a final project.
- The exercises will be evaluated during a mid-term attestation.
- The evaluation of the projects will include a final presentation