Module Number

ML-4503 		Module Title

Advances in Multimodal Learning 		Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Paper presentation und written report
Content

This seminar will cover most recent advancements and publications in multimodal learning, which is the integration of multiple data sources or multiple modalities for more complex machine learning applications. This can also include reviews of emerging techniques, including unsupervised approaches, deep learning, transfer learning, and reinforcement learning to combine multiple modalities such as images, audio, video,
joint feature learning, and natural language processing. It can further cover techniques for data fusion and the role they play in successful applications of multimodal learning. Students will have an opportunity to evaluate and experiment with public code if available. The goal is to develop a better understanding of the possibilities and challenges of multimodal learning.
Objectives

Students gain an overview of current trends and research in multimodal Learning. After this course, students should be able to understand, reflect, and communicate over current research papers on multimodal learning.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation ML-4103 Deep Learning (formerly: Deep Neural Networks; INFO-4182)
Lecturer / Other Kuehne
Literature

-
Last offered unknown
Planned for Sommersemester 2025
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV