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Home Teaching Winter Semester 2019 Practical Course: Expert-Level Deep Learning for Computer Vision and Biomedicine (10 ECTS)

Practical Course: Expert-Level Deep Learning for Computer Vision and Biomedicine (10 ECTS)

Winter Semester 2019/2020, TU München

Please send applications (including learning goals, programming skills description, code, grade transcripts - see preliminary meeting slides) to dlpractice[at]vision.in.tum.de

Organizers: Vladimir Golkov, Prof. Dr. Daniel Cremers

The preliminary meeting (not obligatory) takes place on Monday, 8 July 2019 at 4pm in MI Hörsaal 3 (room 00.06.011). Slides from the preliminary meeting from the summer semester (similar to winter semester, but note the different dates) can be downloaded here.

Registration through the TUM Matching system is done from 19 until 24 July 2019. Details can be found here and here. Sending us an email with sufficient info about yourself ideally several days before you use the Matching website and at the latest until 27 July is crucial for matching success. Students who did not register or did not get matched can contact us directly.

Course Description

In this course, we will develop deep learning algorithms for concrete applications in the field of computer vision and biomedicine. The main purpose of this course is to gain practical experience with deep learning, and to learn when, why and how to apply it to concrete, relevant problems. The topics will include:

  • Machine learning, neural networks, deep learning
  • Standard and advanced network architectures
  • Tasks beyond supervised learning
  • Design of architectures, choice of loss functions, tuning of hyperparameters.

The projects will be geared towards developing novel solutions for real open problems. Projects with various interesting problems and data representations will be offered.

If you want to propose an own project rather than choosing from the projects that we will offer, please discuss with us before 20 July 2019.

Prerequisites

Good programming skills. Eagerness to acquire and deepen knowledge about how to solve complex problems with machine learning. Passion for mathematics. The course will be focused on practical projects, thus previous knowledge of Python and array programming in NumPy (or in Matlab or similar) is desired. Having also good soft skills (or the willingness to acquire them quickly) and using them is a prerequisite.

Knowledge of deep learning and computer vision is recommended/required. Knowledge of biomedicine is NOT required and can be acquired during this practical course. However, the requirements listed above (e.g. good programming skills, soft skills) are mandatory.

Important soft skills include communication skills, the ability to identify what is unclear, to figure out what questions need to be asked to clarify it, to formulate the questions clearly, and to ask the tutor without hesitation. Communicating well and strategically is an important rule of the practical course. Almost all difficulties experienced by students are due to not following these rules.

Course Structure

In the first three weeks, there will be lectures every week, focusing on theoretical and practical concepts related to deep learning. During the semester, the students will work in groups on practical deep learning projects. Each group will consist of about 1-2 students, and will be supervised by one of the tutors. At the end of the semester, each group will present their project with a following Q&A session. There will be no additional written or oral exam. Both the theoretical and practical part of the project will be considered in the final grading. The course schedule is detailed below.

Course Schedule

There will be three lectures in the beginning of the semester.
Time: Tuesdays 2-4pm
Room: 03.13.010

15 October: Machine Learning; Artificial Neural Networks; Network Training; Convolutional Neural Networks; Q&A about Deep Learning
22 October: Recap; Network Architecture Design; Understanding and Visualizing; Q&A about Deep Learning
29 October: Recap; Programming; Evaluating; Q&A about Deep Learning
05 November: Q&A about Deep Learning

Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, SWS) are NOT a measure of project work time, but merely of classroom time.

Literature

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching

info@vision.in.tum.de