Machine Learning for Image Analysis (Sommersemester 2024)
Dozent:
Prof. Dr. Dagmar Kainmueller
(Integrative Imaging Data Science)
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2024 - 30.04.2024
- Lehrform: Vorlesung / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- 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
- 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
- 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
- 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
- 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
NOTE: we switch to room L.1-02!!
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 and object detection. Topics will include supervised and self-supervised learning with convolutional and transformer neural networks, model interpretability and generative models.
Moodle Link: https://moodle.hpi.de/course/view.php?id=733
Voraussetzungen
Prerequisites: Basic knowledge of linear algebra and multivariate analysis. Basic programming skills in Python. Basic knowledge of machine learning, comprising linear and logistic regression, regularized regression, and gradient descent.
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 during the course. There will be pen&paper exercises as well as programming exercises.
Termine
NOTE: FIRST SESSION 04/16/2024
Tuesdays
morning session 11am-12:30pm &
afternoon session1:30pm-3pm
Room L-1.02
For further questions please contact christoph.karg(at)hpi.uni-potsdam.de
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