course-details-portlet

IDIG4225

Specialisation in Multimoddal Data Analysis

Choose study year
Credits 7.5
Level Second degree level
Course start Autumn 2025
Duration 1 semester
Language of instruction English
Location Gjøvik
Examination arrangement Oral exam

About

About the course

Course content

In this course, we will cover state-of-the-art methods and techniques for the integration and interpretation of data within and across modalities. The course will focus on hybrid audio-visual and text models for various multimodal applications using machine learning and deep learning techniques. Actual topics related to multimodal semantic and contextual data analysis and their applications in audio-visual and text domains will be covered and may include but are not limited to:

- Multimodal architectures, frameworks, and tools

- Multimodal data representation

- Embeddings and Transformers

- Multimodal data fusion

- Semantic and Contextual understanding

-- Case study on multimodal sentiment analysis

-- Case study on clinical natural language processing

-- Case study on speaker identification and recognition

Learning outcome

On completion of this course the students will have the following skills, knowledge, and general competence:

Knowledge

- Possess advanced knowledge within the area of multimodal data analysis

- Be able to identify semantic and contextual information across modalities

- Become familiar with frameworks and tools

- Get acquainted with solutions to challenging applications and open issues

Skills

- Be able to critically review and analyze existing models

- Be able to use and apply relevant methods and techniques across modalities

- Be able to propose innovative solutions to assigned problems

General Competence

- Improved understanding of the domain

- Exposure to various tools and techniques

- Exposure to open and challenging research topics

Learning methods and activities

We will have seminars using a blended learning approach with a mix of conventional lectures and flipped Classroom and in-class activities. The students will work individually or in groups and are provided with reading material.

  • There will be lectures by the course instructor and guest lectures by invited experts.
  • Student/groups presentations on selected topics

Compulsory requirements:

Each student/group needs to make a short introductory presentation on the topic and provide a detailed presentation of the work at the end of the course.

Compulsory assignments

Mandatory project report

Compulsory assignments

  • Project Report

Further on evaluation

Grades: A-F

  • Mandatory project report (is mandatory to sit in the exam)
  • 30 min Individual oral exam (counts 100% towards the grade, evaluated by lecturers)

Oral examination based on project work and course material.

No re-sit.

Specific conditions

Admission to a programme of study is required.

Required previous knowledge

Python programming and previous courses on AI/machine learning or deep learning, such as PROG2051 - Artificial Intelligence

Course materials

Research papers, online tutorial/GitHub links, and lecture slides. A selection of research papers will be presented at the start of the course. Research papers and other relevant teaching material used in the seminars will be made available electronically.

Subject areas

  • Computer Science

Contact information

Course coordinator

Department with academic responsibility

Department of Computer Science

Examination

Examination

Examination arrangement: Oral exam
Grade: Letter grades

Ordinary examination - Autumn 2025

Oral exam
Weighting 100/100 Examination aids Code E Duration 30 minutes

Re-sit examination - Summer 2026

Oral exam
Weighting 100/100 Examination aids Code E Duration 30 minutes