Prof. Nassir Navab
Machine Learning in Medical Imaging
, Dr. Shadi Albarqouni
, Anees Kazi
, Roger Soberanis
, Anjany Sekuboyina
, Ashkan Khakzar
, Matthias Grimm
, Walter Simson
Master Practical Course Module IN2106
CAMP Seminar Room, 03.13.010
- 18-04-2019: Projects are assigned! Please contact your tutor this week to define a regular weekly meeting.
- 14-04-2019: Projects are announced! Please, send us your preferences (at least three preferences) by email to email@example.com by Thursday, April 18th. Please make sure the subject of your email is "MLMI_Projects_Preferences". Otherwise, it won't be considered.
- 05-02-2019: Registration, through TUM Matching Platform, should be done between 07.02.19 to 13.02.19
- 11-01-2019: An introductory meeting: Monday, 04.02.2019 (12:30-13:15) in CAMP Seminar Room, 03.13.010.
- 08-01-2019: 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. the 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.