Course - Specialisation in Multimoddal Data Analysis - IDIG4225
IDIG4225 - Specialisation in Multimoddal Data Analysis
About
New from the academic year 2023/2024
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:
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:
Applied Computer Science (MACS)
Computational Colour and Spectral Imaging (MSCOSI)
Recommended previous knowledge
Good knowledge of machine learning/deep learning and python programming
Required previous knowledge
Python programming and previous courses on AI/machine learning or deep learning.
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.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: English
Location: Gjøvik
- Computer Science
Department with academic responsibility
Department of Computer Science
Examination
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"