Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Applied Machine Learning for Digital Health (Sommersemester 2023)

Dozent: Dr.-Ing. Matthieu-Patrick Schapranow (Digital Health - Personalized Medicine)
Tutoren: M.Sc. Florian Borchert M.Sc. Aadil Rasheet
Website zum Kurs: https://hpi.de/digital-health-cluster/teaching/archive/summer-term-2023/applied-machine-learning-for-digital-health.html

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.04.2023 - 07.04.2023
  • Lehrform: Seminar
  • Belegungsart: Pflichtmodul
  • Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

Digital Health MA
Data Engineering MA
IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
Software Systems Engineering MA


The purpose of this seminar is to help you to broaden your expertise in Machine Learning (ML) and Artificial Intelligence (AI) and apply selected methods to real-world use cases. You will select your project preference from a list of seminar topics presented in the kick-off event. The assigned project topic defines the focus of your individual contribution throughout the remainder of the seminar.  As a result, you will broaden your ML/AI skills on a real-world digital health use case, apply selected ML/AI methods, and evaluate and interprete your obtained results.

Your tutors will introduce selected ML/AI technologies and tools to you, which are relevant for your chosen seminar projects. You will acquire hands-on experience with these tools and apply them to your chosen real-world scenarios and realistic data sets. Please bear in mind: Some of the used data sets might require you to either sign-up on a webpage, agree to follow data handling steps, sign a data use or confidentially agreement, or similar aspects. We will equip you with the required ML/AI expertise and provide you access to additional materials for your chosen projects.

We expect you to a) deep dive in the required ML/AI technology, b) conduct research on related work in the specific field, c) design and apply your own ML/AI approach, and d) evaluate your approach and compare it to results from related work. 

You can expect from us a continous coaching with regards to your select project topic throughout the whole semester, e.g. in regular appointments with your tutor. Furthermore, we will provide you guidance for improving your presentation and scientific writing skills. You will share your individual results in an intermediate and a final presentation with all participants. The presentation will help you to communicate your approach and intermediate results to others as well as to receive individual feedback on your approach and individual progress. Ultimately, you will document your findings in a scientific report at the end of the seminar.


Max. number of participants defined by the number of provided topics. After the kickoff event in the first course, you have to send us your preferred seminar topics (due date will be mentioned in the kickoff slides). Afterwards, you will be assigned to one of your preferred topics, which needs to be confirmed through official course enrollment by you.


The final grading will be determined by the following individual parts, each of them must be passed individually: 

  • Seminar results, i.e. intermediate + final presentation conducted during the seminar slots as well as research prototype (40%), 
  • Research article about your individual contribution submitted at the end of the seminar (40%), and
  • Individual commitment throughout the seminar (20%).


Topics and selection procedure will be presented during the kickoff event. For latest details, please refer to the course webpage.   

  • Dates & times: Tue & Thu 1.30pm-3.00pm s.t.
  • Kickoff courses: Thu Apr 20, 2023 @ 1.30pm s.t.
  • Place:G1 E15/16