Prof. Nassir Navab
Machine Learning in Medical Imaging
, Shadi Albarqouni
, Christoph Baur
, Magda Paschali
, Huseyin Coskun
, Abhijit Guha-Roy
, Roger Soberanis
Master Practical Course Module IN2106
CAMP Seminar Room, 03.13.010
- 03-05-2018: There is no Intermediate Presentation I Session. We will have one Intermediate Presentation on 19th June 2018.
- 09-04-2018: Projects are assigned! Please contact your tutor by this week to define a regular weekly meeting.
- 03-04-2018: Projects are announced! Pleaae send us your preferences (at least three preferences) by email to firstname.lastname@example.org by Friday, April 5th. Please make sure the subject of your email is "MLMI_Projects_Preferences". Otherwise, it won't be considered.
- 29-01-2018: Registration, through TUM Matching Platform, should be done between 09.02. to 14.02.18
- 13-01-2018: An introductory meeting: Monday, 29.01.2018 (10:00-11:00) in CAMP Seminar Room, 03.13.010.
- 13-01-2018: 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 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: 20.
- 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 impact of the exercises on the final grade is 30%.
- The evaluation of the projects will include a final presentation. The impact of the final project on the final grade is 70%.
- Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.