Erasmus Mundus Joint Master's Programme in Imaging, 120 credits, 120 credits

Aim

The purpose of the programme is to provide in-depth knowledge in the multi-dimensional image and video processing with a special focus on the machine learning and deep learning. The programme will cover theoretical fundamentals of machine learning and deep learning as well as image, video, and multi-dimensional visual signals topics. The main focus is on the use of machine learning, deep learning, and artificial intelligence tools on visual signals. Integrating various areas of knowledge can help provide a well-rounded understanding of the capabilities and limitations of visual AI technologies.

Programme objectives

OUTCOMES ACCORDING TO THE HIGHER EDUCATION ORDINANCE FOR A MASTER'S DEGREE (TWO YEARS)

Knowledge and understanding
For a Degree of Master students must
\- demonstrate knowledge and understanding in their main field of study, including both broad knowledge in the field and substantially deeper knowledge of certain parts of the field, together with deeper insight into current research and development work; and
\- demonstrate deeper methodological knowledge in their main field of study.

Skills and abilities
For a Degree of Master students must
- demonstrate an ability to critically and systematically integrate knowledge and to analyse, assess and deal with complex phenomena, issues and situations, even when limited information is available;
- demonstrate an ability to critically, independently and creatively identify and formulate issues and to plan and, using appropriate methods, carry out advanced tasks within specified time limits, so as to contribute to the development of knowledge and to evaluate this work;
- demonstrate an ability to clearly present and discuss their conclusions and the knowledge and arguments behind them, in dialogue with different groups, orally and in writing, in national and international contexts; and - demonstrate the skill required to participate in research and development work or to work independently in other advanced contexts.

Judgement and approach
For a Degree of Master students must
- demonstrate an ability to make assessments in their main field of study, taking into account relevant scientific, social and ethical aspects, and demonstrate an awareness of ethical aspects of research and development work;
- demonstrate insight into the potential and limitations of science, its role in society and people’s responsibility for how it is used; and
- demonstrate an ability to identify their need of further knowledge and to take responsibility for developing their knowledge.

SPECIFIC OUTCOMES FOR THE ERASMUS MUNDUS JOINT MASTERS IN IMAGING WITH VISUAL AI SPECIALIZATION

After the completion of the programme the student should
• Show thorough understanding of current research and development within the computer engineering area.
• Demonstrate familiarity with development trends and current applications.
• Show high proficiency in software implementation of algorithms and in the use of relevant software packages
• Show proficiency in software implementation of algorithms and in the use of foundational software packages for image processing computer vision, multi-view geometry, computer graphics, and data visualization.
• Show high proficiency in explaining and justifying several important artificial intelligence and machine learning methods.
• Demonstrate good skills in all aspects of extraction of knowledge from data.
• Show high proficiency in analyzing and optimizing the technical performance of different systems and advanced services.
• Demonstrate advanced skills in simulation, modelling and analysis of algorithms and systems

Content

Specialization “Immersive Imaging”

- Computer Engineering MA, at Mid Sweden University:
Probability and Random Processes, 6 credits
Quantitative Research and Development, 6 credits
Applied Optimization, 6 credits
Computer Graphics, 6 credits
Datamining and Machine Learning, 6 credits
Computer Vision and Multiview Geometry, 6 credits
Neural Networks and Deep Learning, 6 credits
Image analysis, 6 credits
Visualization, 6 credits
Signal and Image Processing, 6 credits *

* If the student is missing a course in databases, the student must take the course Databases - Modelling and Implementing, 6 credits in the first period in the first semester and Signal and image processing, 6 credits is omitted.

- Computing Sciences, at Tampere University – 30 credits (ECTS) from elective courses listed below (excluding Master thesis)
Fourier Optics, 5 credits
Imaging Sensors and Systems, 5 credits
Advanced Image Processing, 5 credits
Imaging Systems Lab, 5 credits
Deep Learning for Computer Vision, 5 credits
3D and Extended Reality, 5 credits
Vector Space Methods for Signal and Image Processing, 5 credits
Deep Learning, 5 credits
Master Thesis, 30 credits

Specialization "Visual AI"

- Computer Science and Engineering, at Politecnico di Milano
Software Engineering 2, 5 credits
Artificial Neural Networks and Deep Learning, 5 credits
Foundations of Operations Research, 5 credits
Image Analysis and Computer Vision, 5 credits
Fundamentals of Multimedia Signal Processing, 5 credits
Advanced Computer Architectures, 5 credits
Computing Infrastructures, 5 credits
Machine Learning, 5 credits
Applied Statistics, 10 credits
Two elective courses (in total, 10 credits)

- Computer Engineering MA, at Mid Sweden University:
Augmented and Virtual Reality, 6 credits
Advanced Image Processing, 6 credits
Multidimensional Visual Representation and Compression, 6 credits
Quantitative Research and Development, 6 credits
Machine Learning for Visual Media Applications, 6 credits
Degree Project, 30 credits

Entry requirements

Degree of Bachelor (at least 180 credits), Degree of Bachelor of Science in Computer or Electrical Engineering (at least 180 credits), or equivalent, with at least 30 credits in Mathematics/Applied Mathematics, including courses in probability theory and statistics and linear algebra, and 60 credits in Computer Engineering or equivalent including a course in object oriented programming and one of the following two courses: databases or signal and image processing.

English course 6 from Swedish Upper Secondary School (Gymnasium) or the equivalent.

Description of programme

The programme is offered as full time studies, during two years.

Selection rules and procedures

Alternative selection, see "Description of alternative selection".

Description of alternative selection

The selection of students is done according to the following procedure:

The EMJM IMAGING Consortium Committee appoints a Selection Committee, composed of one representative of each Partner and the Consortium-wide Programme Manager. The Selection Committee formulates selection criteria and performs the evaluation of the eligible candidates accordingly.

The selection criteria must include

1) evaluating the academic excellence,

2) applicant’s motivation letter, and

3) provided recommendation letters.

The criteria are combined into a compound evaluation grade based on the weighing coefficient of each criterion.

After ranking the applications according to the selection criteria, the Selection Committee defines the Main List, the Reserve List, and the Non-Selected List of applicants.

Programme with restricted admissions

Specific entry requirements for each course within the programme are listed in the respective syllabus. In order to be able to read the second year, it is also required that the student attained at least 45 credits at year-end 2 at the beginning of grade 2. Students who do not meet these requirements should contact the programme department for assistance with planning.

Teaching and examination

Teaching and examination procedures are stated in the syllabus of each course. The language used is English.

Title of qualification

Masterexamen

Masterexamen med huvudområdet datateknik

translated into

Degree of Master of Science (120 credits) with a major in Computer Engineering

Other information

There is a possibility for changes concerning the time, name, content, level and distribution of the credits within the courses during the time the programme is running.

This programme corresponds to the "Visual AI" and “Immersive Imaging” Study Tracks within the EMJM IMAGING program accepted by the European Commission.

In “Visual AI” track, the students spend their first year at Politecnico di Milano and their second year at Mid Sweden University.

In “Immersive Imaging” track, students spend their first year at Mid Sweden University and their second year at Tampere University.

In another study track named "Image modelling and data-intensive imaging", students study at Politecnico di Milano and Tampere University.