Type of course

The course can be taken as a singular course.

Registration is open for UiT students and members of NORA Research School.

It will be conducted as a concentrated course in the style of summer school, 10th -14th of June 2025 in Høgskolen i Østfold, Fredrikstad.

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years (300 ECTS) or 3 (180 ECTS) + 2 years (120 ECTS) or equivalent may be admitted.PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee. Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement /English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb. External applicants apply for admission through SøknadsWeb. All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master’s degree diploma. These students are also required to pay the semester fee.

Recommended prerequisites: Programming skills in python and hands on knowledge of python programming for deep learning.

Application code: 9317, application deadline: May 1st

The course is limited to 40 places. Qualified applicants are ranked on the basis of a lottery if there are more applicants than available places.

Course content

This course will provide both a fundamental understanding of the techniques and methods used in multi-modal learning and the most recent innovations within the field. The course will cover multi-modal learning in the context of important data domains such as image, text, and time series data, and give the students a deeper understanding of the theory that underpins current multi-modal methods. The course will consists of 5 days of teaching with both lectures and practical components.

A take-home exam will be given that will be used for evaluation.

Core concepts

• History of multi-modal learning
• Motivation and fundamental concepts
• Encoders, data fusion, and loss functions

Discriminative multi-modal learning

• Classification-based methods
• Clustering-based methods

Self-supervised multi-modal learning

• Contrastive methods
• Non-contrastive methods
• Masking-based methods
• Data augmentation

Generative multi-modal learning theory

• Auto-regressive methods
• Predictive methods

Advanced multi-modal learning theory

• On the design of multi-modal loss functions
• Understanding data fusion multi-modal learning
• Nosiy and missing data in multi-modal learning

Relevance of course in program of study: Fusing information from multiple modalities is a fundamental challenge in machine learning. However, recent works have achieved impressive performance on a wide range of different tasks involving modalities from variety of different source such as images, time series, and text. Recent multi-modal approaches heavily rely on deep neural networks to process various different data types and fuse them together to a common representation that contains the complementary information found in the different sources. Performing this fusion in a reliable and precise manner is key to achieve good performance. Furthermore, the added complexity of having multiple neural networks for different modalities exacerbates already existing neural network-related challenges such as a lack of explainability and a limited capability for uncertainty modeling.

Objectives of the course

Knowledge - The student is able to

• Describe advanced multi-modal techniques
• Describe the role of domain-knowledge in multi-modal learning
• Describe the development of multi-modal learning
• Discuss recent development in the field
• Discuss advanced multi-modal learning in an applied setting.

Skills - The student is able to

• Explain the fundamental ideas behind multi-modal learning
• Apply the learned material to new applications or problem settings
• Use multi-modal learning for research and industrial settings using software libraries such as e.g. Pytorch or TensorFlow
• Make appropriate method and architecture choices for a given application or problem setting

General competence - The student is able to

• Give an interpretation of recent developments and provide an intuition of the open questions in the field of multi-modal learning
• Show an understanding of why multi-modal learning has shown great improvements over the last couple of years

Language of instruction and examination

Teaching methods

Lectures: 20 hours

Self-study sessions: 40 hours

Project work: spread over 8 weeks - net time 50 hours

Hands-on sessions: 10 hours

Net effort: ( 120 hours)

Final exam