Machine Learning in Medical Imaging
, Diana Mateus
, Sailesh Conjeti
, Shadi Albarqouni
, Marco Esposito
, Pascal Fallavollita
Master Practical Course Module IN2106
Time: MONDAYS 10.00 - 12.00
- Lectures and assignments are password protected
- To register for the course, please use the TUM-Matching-System
- The preliminary meeting will take place on the 25th of January at 13:00 in MI 03.13.010 and will have an approximate duration of 1/2 hour
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 in the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realised in couples. The programming language for the projects is to be decided in agreement with your supervisor.
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
- Interested students should attend the introductory meeting to enlist in the course.
- Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached.
- Maximum number of participants: 16.
- Strong knowledge of MATLAB or other programming language
- Strong background in image processing
- New this year Some of the exercises will be in Python, althoug 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 Python is advantageous (in case you decide to use python for your final project): numpy, scipy, scikit-learn.
- Basic knowledge of naive machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...