Applied Machine Learning for Digital Health (Sommersemester 2020)
Lecturer: Dr.-Ing. Matthieu-Patrick Schapranow
(Digital Health - Personalized Medicine)
- Weekly Hours: 4
- Credits: 6
- Enrolment Deadline: 06.04.2020-27.04.2020
- Teaching Form: Lecture / Seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: English
Programs & Modules
- OSIS-Konzepte und Methoden
- OSIS-Techniken und Werkzeuge
The purpose of this seminar is to help you to broaden your expertise in Machine Learning (ML) and Artificial Intelligence (AI). Therefore, well will introduce to you selected ML&AI technologies and tools relevant for your chosen seminar projects. You will acquire hands-on experience with these tools and apply them to real-world scenarios. We will equip you with the required ML&AI expertise and provide you access to materials for your chosen projects. We expect from you to deep dive in the required ML/AI technology, to learn about related work, to design and apply your own ML/AI approach, and to evaluate your approach and compare it to results from related work.
You will select your preferred project topic from a list of topics presented in the kick-off lecture. We will coach you throughout the whole semester with regards to the chosen project. Furthermore, we will support you to improve your research and presentation skills. You will share your results in intermediate and final presentations together with all participants. You will document your final results in a scientific way.
Please refer to the website of the seminar for latest updates.
- Due to the global spread of COVID19 (latest news), a university wide non-presence mode was announced by our chancellor. Therefore, we provide remote dial-in details for all meetings and consultation of the giving seminar to protect your health. Please refer to the website of the seminar for instructions. Please help to flattern the curve by staying home and protect your health!
The final grading will be determined by the following individual parts, where each of them must be passed individually:
- Intermediate presentation and final presentation (40%)
- Research article (40%)
- Individual commitment (20%)