Advanced Machine Learning Seminar (Wintersemester 2023/2024)
Dozent:
Prof. Dr. Christoph Lippert
(Digital Health - Machine Learning)
,
Sumit Shekhar
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.10.2023 - 31.10.2023
- Prüfungszeitpunkt §9 (4) BAMA-O: 01.03.2024
- Lehrform: Seminar
- 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
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/16mk24Q0JCdwOfsE0iodwnH9MVuOJoNDv7x4_f_HvC7A/edit?usp=sharing--
If you can't make it to the kick-off meeting on 18 October, but still want to participate in the seminar or if you have any other questions related to the seminar, feel free to contact Sumit (sumit.shekhar(at)hpi.de)
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.
Literatur
Project list link: https://docs.google.com/spreadsheets/d/16mk24Q0JCdwOfsE0iodwnH9MVuOJoNDv7x4_f_HvC7A/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 on 18 October, 4-5pm in G1.E15/16
Zoom meeting Details:
Join Zoom Meeting
https://mssm.zoom.us/j/86501140876?pwd=bk9YZG54dzhsa2V2Z01UQTFDeTg5Zz09
Meeting ID: 865 0114 0876
Passcode: 853479--
If you can't make it to the kick-off meeting but still want to participate in the seminar or if you have any other questions related to the seminar,
feel free to contact Sumit (sumit.shekhar(at)hpi.de)
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