Advanced Medical Machine Learning Seminar (Sommersemester 2024)
Dozent:
Prof. Dr. Christoph Lippert
(Digital Health - Machine Learning)
,
Sumit Shekhar
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2024 - 30.04.2024
- Lehrform: Vorlesung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
- Maximale Teilnehmerzahl: 10
Studiengänge, Modulgruppen & Module
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-K Konzepte und Methoden
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-T Techniken und Werkzeuge
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-S Spezialisierung
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-K Konzepte und Methoden
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-T Techniken und Werkzeuge
- SAMT: Software Architecture & Modeling Technology
- HPI-SAMT-S Spezialisierung
- DANA: Data Analytics
- HPI-DANA-K Konzepte und Methoden
- DANA: Data Analytics
- HPI-DANA-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-S Spezialisierung
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-C Concepts and Methods
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-T Technologies and Tools
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-S Specialization
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- 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
- MALA: Machine Learning and Analytics
- HPI-MALA-C Concepts and Methods
- MALA: Machine Learning and Analytics
- HPI-MALA-T Technologies and Tools
- MALA: Machine Learning and Analytics
- HPI-MALA-S Specialization
Beschreibung
Kick-off event on 11 April!
This seminar consists of semester-long research projects. The projects span topics from core machine learning research, such as generative models, uncertainty quantification, and interpretability; as well as applications in the biomedical and health sciences, such as epidemiological N-of-1 trials, genetics, and medical imaging. Students are expected to work closely with their individual supervisors (PhD students and PostDocs at the Digital Health - Machine Learning group), make substantial progress on their task, and give a presentation at the end of the semester. Especially successful projects may additionally lead to the publication of a scientific paper.
Students are required to have good coding skills (language will depend on the topic, but mostly Python and R) and have at least a basic understanding of modern machine learning, e.g. through the Deep Learning lecture at HPI or similar online courses.
project list link https://docs.google.com/spreadsheets/d/1o2W-5qLHpLeZ5B9Se45eGjoWtRwXC7BQegLmFDQg9n0/edit?usp=sharing
Voraussetzungen
Precise requirements differ between the different research projects. In all cases, basic skills in machine learning/deep learning and/or statistics are highly preferred. Ambitious students may take this seminar in parallel with the Introduction to Deep Learning course.
Literatur
project list link https://docs.google.com/spreadsheets/d/1o2W-5qLHpLeZ5B9Se45eGjoWtRwXC7BQegLmFDQg9n0/edit?usp=sharing
Leistungserfassung
Students will work on a project for the course, and the seminar will end with a short presentation and/or a short written report. Details to follow.
Termine
Kick-off event details:
When: 11th April, 9:15 a.m.
Where: pool room G2.U10/14
or via zoom using the below link:
https://zoom.us/j/5639734929?pwd=MWlnWStLRWVCdzk3eGhIb3dUTWlhUT09
Contact: teaching-lippert(at)hpi.de
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