Advanced Machine Learning Seminar (Wintersemester 2022/2023)
Lecturer:
Prof. Dr. Christoph Lippert
(Digital Health - Machine Learning)
,
Matthias Kirchler
(Digital Health - Machine Learning)
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 01.10.2022 - 31.10.2022
- Examination time §9 (4) BAMA-O: 31.03.2023
- Teaching Form: Seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: English
- Maximum number of participants: 10
Programs, Module Groups & Modules
- 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
Description
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.
If you have questions regarding the structure or if this seminar is appropriate for you, feel free to contact Matthias.
Requirements
Precise requirements differ between the different research projects. In all cases, basic skills in machine learning/deep learning and/or statistics are highly preferred.
Examination
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.
Dates
We will have an online kick-off meeting on Oct 24, 5PM (Zoom link will be posted here). After that, supervisors and students arrange regular individual meetings.
If you can't make it to the kick-off meeting but still want to participate in the seminar, please contact Matthias.
Link to the kick-off:
uni-potsdam.zoom.us/j/68661434702
Passcode: 39666547
Projects & presentations slides are here: docs.google.com/spreadsheets/d/1BLyKtm2N8mXSTZuJrFM6BMWgMutwVHGhkYeW2wvKL_U/edit
Please fill in your preferences and contact the corresponding supervisors as soon as possible. We want to finish the project-finding phase in the next few days, latest by the end of the week!
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