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Home Teaching Winter Semester 2017/18 Practical Course: Hands-on Deep Learning for Computer Vision and Biomedicine (6h / 10 ECTS)

Practical Course: Hands-on Deep Learning for Computer Vision and Biomedicine (6h / 10 ECTS)

Winter Semester 2017/2018, TU München

Please direct all questions regarding this practical course to golkov[at]in.tum.de

Organizers: Vladimir Golkov, Prof. Dr. Daniel Cremers

The preliminary meeting (not obligatory) took place on Tuesday, 11 July 2017 at 14:00 in room 5620.01.102 (Interims-Hörsaal 2). A summary of the preliminary meeting can be downloaded here.

Registration through the TUM Matching system is done on 14-19 July 2017. Details can be found here. Sending us an email with sufficient info about yourself until 21 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 and implement 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
  • Convolutional neural networks
  • Recurrent neural networks
  • 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 different 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 21 July.

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 Matlab or similar) is desired.

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 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, 14:00-16:00.
Room: 02.09.023

12 October: Machine Learning, Artificial Neural Netoworks
24 October: Recap; Network Training; Convolutional Neural Networks; Network Architecture Design
(31 October: Public Holiday)
7 November: Recap; Programming; Understanding and Visualizing

Literature
Last edited 11.07.2017 18:16 by Vladimir Golkov