Practical Course: Hands-on Deep Learning for Computer Vision and Biomedicine (6h / 10 ECTS)
Winter Semester 2017/2018, TU München
This is the winter semester 2017/2018 course. For the summer semester 2018 course, see here.
Please direct all questions regarding this practical course to golkov[at]in.tum.de
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.
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.
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.
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.
There will be three lectures in the beginning of the semester.
Time: Tuesdays, 14:00-16:00.
17 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