Hasso-Plattner-InstitutSDG am HPI
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Applied Machine Learning for Digital Health (Sommersemester 2022)

Lecturer: Dr.-Ing. Matthieu-Patrick Schapranow (Digital Health - Personalized Medicine)
Course Website: https://hpi.de/en/digital-health-center/teaching/summer-term-2022/applied-machine-learning-for-digital-health.html

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.04.2022 - 30.04.2022
  • Examination time §9 (4) BAMA-O: 19.08.2022
  • Teaching Form: Seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering MA
Data Engineering MA
Digital Health MA
  • Digital Health
    • HPI-DH-DS Data Science for Digital Health
  • APAD: Acquisition, Processing and Analysis of Health Data
    • HPI-APAD-T Technologies and Tools
  • APAD: Acquisition, Processing and Analysis of Health Data
    • HPI-APAD-S Specialization
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-T Technologies and Tools
  • DICR: Digitalization of Clinical and Research Processes
    • HPI-DICR-S Specialization

Description

The purpose of this seminar is to help you to broaden your expertise in Machine Learning (ML) and Artificial Intelligence (AI) and apply it to selected real-world use cases.

Therefore, we 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 real-world scenarios on realistic data sets. Please bear in mind: to allow you access to real-world data, some of the 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 materials for your chosen projects. We expect you to deep dive in the required ML/AI technology, to do research on related work in the specific field, to design and apply your own ML/AI approach, and to evaluate your approach and compare it to results from related work. 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.

You will select your project preference from a list of seminar topics presented in the kick-off event. We will coach you throughout the whole semester with regards to the chosen project, i.e. you will have regular appointments with your tutor. Furthermore, we will provide guidance for improving your research and presentation skills throughout the seminar. Therefore, you will share your results in an intermediate and a final presentation with all participants. The presentation will help you to communicate your approach and intermediate results as well as to receive individual feedback on the approach and progress. Ultimately, you will document your findings in a scientific report at the end of the seminar.

Please refer to the website of the seminar for latest updates.

Requirements

Participants should have attended foundation lectures on statistics, data analysis and machine learning theory prior joining this specialization seminar. Furthermore, it would be helpful, if particpants are intested to make a deep dive into very specific medical topics to support medical experts improving current therapy options. No worries: you will be guided by subject-matter experts and supervisors, no need for a completed medical education, but biology recap or courses in digital health would be beneficial.  

Learning

The number of participants in the seminar is limited by the number of presented topics. If the number of interested participants exceeds the number of topics, candidates will be selected by lot. The seminar is planned in presence respecting current hygenic measures to fight the COVID-19 pandemic, potentially selected online slots.

Examination

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 + research prototype  (40%) 
  • Research article submitted by the end of the seminar (40%)
  • Individual commitment (20%)

Dates

  • First course: Tue Apr 26, 2022
  • Location: Campus III, G1.E15/16

Please refer to the website of the seminar for latest updates.

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