IDIG4225 - Specialisation in Multimoddal Data Analysis


Examination arrangement

Examination arrangement: Oral exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Oral exam 100/100 30 minutes E

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:


- 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


- 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

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.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Computer Science
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science


Examination arrangement: Oral exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Oral exam 100/100 E
Room Building Number of candidates
Summer UTS Oral exam 100/100 E
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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