Machine Learning for Image Analysis (Sommersemester 2023)
Dozent:
Prof. Dr. Dagmar Kainmueller
(Integrative Imaging Data Science)
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2023 - 07.05.2023
- Prüfungszeitpunkt §9 (4) BAMA-O: 04.08.2023
- Lehrform: Vorlesung / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- 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
- 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
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- 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
- IT-Systems Engineering
- IT-Systems Engineering
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
Beschreibung
Note: Due to the strike of Deutsche Bahn we will have the lecture on 16 May online! You will find the link to the session on Moodle
The course will introduce machine learning methods for image analysis, with a focus on deep learning. The aim is to convey state of the art methodology for solving problems like image classification, semantic segmentation, instance segmentation, object detection, and object tracking. Topics will include supervised and self-supervised learning with convolutional and transformer neural networks, model interpretability, probabilistic models, and generative models. Prerequisites that go beyond a basic knowledge of linear algebra, analysis and probability theory will be covered.
Moodle Link: https://moodle.hpi.de/user/index.php?id=464
Leistungserfassung
We will have a final written exam (100% Grading)
Prerequisite for participation in the exam is attaining at least 50% of the possible points in the exercises druing the course
Termine
Note: Due to the strike of Deutsche Bahn we will have the lecture on 16 May online! You will find the link to the session on Moodle
Tuesdays
morning session 11am-12:30pm &
afternoon session1:30pm-3pm
Room L1.06
Exam 06 September in DHC Seminar Room G3 E15/16
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