Type of course

The course is available as a singular course. The course is also available to exchange students and Fulbright students.

The students must be in a master's program in electronics or artificial intelligence or physics or computer science, or related engineering fields.

Admission requirements

A relevant undergraduate Bachelor Engineering program with minimum 25 credits mathematics, 5 credits statistics, 7,5 credits physics.

Application code: 9371

FYS-2010 Image Analysis, FYS-2006 Signal Processing or FYS-2021 Machine Learning is recommended.

Course content

Introduction to machine vision: fundamentals of image formation and camera parameters. An overview of Image sensing pipeline, Demosaicing, and Compression.

Fundamentals of supervised learning, unsupervised learning, reinforcement learning, and ethics in Deep learning.

Training deep learning models, loss functions, gradients, initialization, evaluation of performance in Deep learning.

Fundamentals of technical and scientific report writing with emphasis on performing experiments and data analysis.

Fundamentals of programming using Python for Deep learning applications.

Recommended prerequisites

Objectives of the course

Knowledge:

This interdisciplinary course should give the candidate a good understanding of fundamentals of machine vision with special focus on application of deep learning in a case study or application.

Skills:

• Candidate will build knowledge in image formation, cameras, and vision sensors.
• Candidate will learn about deep learning and subtopics such as: supervised learning, unsupervised learning, reinforcement learning, and ethics in deep learning.
• Candidate will learn about training deep learning models, loss functions, gradients, initialization, and performance evaluation in deep learning.
• Candidate should be able to understand and use the knowledge from machine vision in their selected application or task.
• Candidate should be able to demonstrate their knowledge using Python.
• Candidate should be able to demonstrate scientific analysis of data or experiments in a case study report.

Language of instruction and examination

Teaching methods

Information to incoming exchange students

This course is open to incoming exchange students.

Study level: Master’s

Admission prerequisites:
This course has admission prerequisites, which are listed under the Admission requirements section. Please review this information carefully before adding the course to your Learning Agreement.

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.