Prof. Nassir Navab
Machine Learning in Medical Imaging
, Dr. Seong Tae Kim
, Ashkan Khakzar
, Azade Farshad
Master Practical Course Module IN2106
Online Lecture (Please find access information in Moodle)
14-05-2020: Please find lecture materials in Moodle
15-04-2020: Project proposals are available
31-01-2020: Slides for the preliminary meeting are available
20-01-2020: Preliminary meeting: Thursday, 30.01.2020 (16:00-16:30) in CAMP Seminar Room, 03.13.010.
20-01-2020: 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.
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).
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
- 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.
Please find lecture materials in Moodle