Practical Course:
Machine Learning in Medical Imaging
Nassir Navab,
Tingying Peng,
Diana Mateus,
Sailesh Conjeti,
Abhijit Guha Roy,
Anees Kazi
Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: 03.13.10
Time: Wednesdays, 10:00-12:00
Course Language: English
Announcements
- Since the TUM matching is over and we are currently in the selection process, please do not send us any further motivation letter or inquiry this semester. Students who are interested in the course but missed the matching please consider to participate in the course next semester.
- Due to the large number of enquiry emails, we are not able to answer all of them. If you would like to register the course, please use TUM matching system, and send a motivation email titled as MLMI motivation plus your name (we will use MLMI motivation as a keyword for email search, so emails with other titles will be automatically ignored), and the content of the letter should be put in the email itself. Please do not attach any additional document like word or pdf, and the letter should be less than 200 words. Due to the limited number of positions, we cannot guarantee a position for every highly-motivated student, so please also consider register the course next semester.
- Registration is open from 03.02.2017 to 08.02.2017 through the TUM Matching Platform.
- The preliminary meeting will take place on 25th of Jan 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.
Schedule
Evaluation
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
Projects
Attestation Schedule
Registration
- 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: 20.
Requirements
- *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 ...
FAQs