TeachingWs17MLMI

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Practical Course:
Machine Learning in Medical Imaging

Prof. Nassir Navab, Shadi Albarqouni, Christoph Baur, Mai Bui, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Mondays, 10-12
Course Language: English

Announcements

  • 10-07-2017: Please register via TUM Matching System within 14.07.2017 - 19.07.2017 .
  • 26-06-2017: An introductory meeting: Monday, 10.07.2017 (16:15-16:45) in CAMP Seminar Room, 03.13.010.
  • 26-06-2017: 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.

Registration

  • 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: 18.

Requirements

  • 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 ...

Schedule (Tentative)

Date Topic By Slides Homework
10.07.2017 Preliminary Meeting Shadi slides  
16.10.2017 Introduction to Machine Learning and Python Shadi + Christoph    
23.10.2017 Dimensionality Reduction and Clustering Anees    
30.10.2017 Logistic, Linear Regression, and Support Vector Machine Roger    
06.11.2017 Random Forests Mai    
13.11.2017 Intermediate Presentations I all tutors    
20.11.2017 Neural Networks (NN) and Auto-Encoders (AE) Abhijit    
27.11.2017 Convolutional Neural Networks Shadi + Christoph    
04.12.2017 Recurrent Neural Networks (RNN/LSTM) Huseyin    
11.12.2017 Intermediate Presentations II all tutors    
21.12.2016        
18.12.2017        
08.01.2018        
15/22/29.01.2018 Final Presentations      

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

ID Project Tutor Assigned Student Material




TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Shadi Albarqouni, Christoph Baur, Mai Bui, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis
Type: Praktikum
Information: Praktikum, SWS: 6, ECTS: 10
Term: 2017WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.15 - 15 Sep 2017 - 12:24 - ShadiAlbarqouni

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