Practical Course:
Machine Learning in Medical Imaging

Nassir Navab, Diana Mateus, Sailesh Conjeti, Shadi Albarqouni, Marco Esposito, Pascal Fallavollita

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location:
Time: MONDAYS 10.00 - 12.00
Course Language: English

Announcements

• Lectures and assignments are password protected
• To register for the course, please use the TUM-Matching-System
• The preliminary meeting will take place on the 25th of January at 13:00 in MI 03.13.010 and will have an approximate duration of 1/2 hour

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

Date	Topic	By	Slides	Homework
11.04.2016	Introduction to Python Bring ANACONDA (Python 3) installed in your computer	Marco Esposito	Course info Intro to Python iPython Notebook	Assignment
18.04.2016	Dimensionality reduction and clustering	Shadi Albarqouni	PCA PCA_Applications Clustering	Assignment data Have fun with faces dataset
25.04.2016	Linear classifiers and evaluation measurements Presentation of projects: Make your choice before Friday April 29	Tingying Peng	LinearClassifier&Evaluation Project List	Exercise_LinearClassifier&Evaluation SAheartData.zip
02.05.2016	Support Vector Machines	Diana Mateus	SVM Lecture Project/Group Assignment	Exercise sheet Two moons dataset digits Dataset
09.05.2016	Random Forests	Loic Peter	Slides	Exercise sheet Code
23.05.2016	Neural Networks and Autoencoders	Fausto Milletari	Slides	Exercise Additional Material
30.05.2016	Deep Learning	Christian Rupprecht	Slides	Exercise
13.06.2016	Attestation I
13.06.2016	Attestation II
18.07.2016	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

Attestation Schedule

Student	Time
Han Changhee	10:00-11:00
Heinzerling Till	10:00-11:00
Jimenez Sanchez Amelia	10:00-11:00
Lisitsyna Anna	10:00-11:00
Meschede Claus	10:00-11:00
Navarro Avila José	10:00-11:00
Nguyen Thi Yen	10:00-11:00
Nikolaev Vitaly	11:15-12:15
Page Vizcaino Josue	11:15-12:15
Paschali Magdalini	11:15-12:15
Raszl Matheus	11:15-12:15
Rychly Leonard Valentin	11:15-12:15
Schnoy Benjamin	11:15-12:15
Singh Prashant	11:15-12:15

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: 16.

Requirements

• Strong knowledge of MATLAB or other programming language
• Strong background in image processing
• New this year Some of the exercises will be 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 ...