Machine Learning for Image Analysis (Sommersemester 2023)

Lecturer: Prof. Dr. Dagmar Kainmueller (Integrative Imaging Data Science)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2023 - 07.05.2023
• Examination time §9 (4) BAMA-O: 04.08.2023
• Teaching Form: Lecture / Exercise
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

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

Examination

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

Dates

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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