Machine Learning in Medical Imaging
, Diana Mateus
, Tingying Peng
, Shadi Albarqouni
, Sailesh Conjeti
, Christian Rupprecht
, Nicola Rieke
Master Practical Course Module IN2106
- Final Presentation (7 mins.) will take place on Wednesday, 15th Feb. 2017 at 10 AM in the Seminar room 03.13.10
- Projects are assigned. Please contact your supervisor!
- Projects are uploaded (Missing ones will be up soon). Now, you need to send three preferences along with their project's ID to Shadi Albarqouni. Deadline to submit your preferences is Monday, 14th Nov. 2016
- The preliminary meeting will take place on 4th of July at 11:00 a.m. Seminar room 03.13.10
- Lectures and assignments are password protected
- To register for the course, please use the TUM-Matching-System
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, Python * or other programment language
- Background in image processing
- Exercises will be held in preferably 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 ...
- Should we send the e-mail as a group? Or should each member of the group send it separately?
- Since we have rather enough projects, every student will be assigned to one project. However, there might be two projects with two members assigned by the course organizers. In short, send your preferences individually.
- Do we have to sort the options by preference, or are the three of them taken with the same priority?
- Yes, you need to sort them based on your preferences. We will do our best to match your first preference.
- For those projects that some knowledge on Deep Learning is required, is it enough with the one we will acquire during the lecture and the assignment or are we supposed to have some extra knowledge?
- Since the deep learning lecture will be held on 21st Dec., you won't have enough time to work on the project. Therefore, it is recommended to do some extra work.
- Is the tutor who is assigning each member of the group his/her particular tasks or are we splitting the work?
- In case if you are assigned with another student as one group, you need to discuss that with your tutor.