Practical Course: Hands-on Deep Learning for Computer Vision and Biomedicine (10 ECTS)
Summer Semester 2018, TU München
Please direct all questions regarding this practical course to golkov[at]in.tum.de
Registration through the TUM Matching system (temporarily offline as of 19 January) is done on 9-14 February 2018. Details can be found here. Sending us an email with sufficient info about yourself until 16 February 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
- 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 16 February.
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.
Choice among many different projects will be offered. Some of these projects will be based on previous knowledge about machine learning, or computer vision, or biomedicine; but many other projects will NOT require such previous knowledge. The students will acquire knowledge during this practical course. However, the requirements listed above (e.g. good programming skills) are mandatory.
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.
There will be three lectures in the beginning of the semester.
Time: Tuesdays, 14:00-16:00.
10 April: Machine Learning, Artificial Neural Netoworks
17 April: Recap; Network Training; Convolutional Neural Networks; Network Architecture Design
24 April: Recap; Programming; Understanding and Visualizing; Evaluating
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.