Machine Vision
About the course
The course is available as a singular course. The course is also available to exchange students and Fulbright students.
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.
Admission requirements
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.
Teaching methods
Language of instruction and examination
EnglishInformation to incoming exchange students
This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements" section".
Bachelor Level
Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412.
Deadline: 15th April
Examination
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