Max amount of FITech students: 30 adult learners
In today’s rapidly evolving technological landscape, the ability to interpret and analyse visual data has become a core skill for engineers across industries. This course offers a comprehensive introduction to Convolutional Neural Networks (CNNs) and modern machine learning techniques tailored for visual recognition tasks.
Related technologies are at the heart of countless modern applications—from self-driving cars and medical diagnostics to smart factories and consumer electronics. As industries increasingly rely on machines to understand and interact with visual environments, engineers equipped with these skills are in high demand.
Whether you’re working in embedded systems, software development, robotics, or product innovation, this course provides the knowledge you need to build intelligent systems that see, understand, and act.
Course contents
- Convolutional neural network architectures
- Convolutional neural network training
- Convolutional neural network deployment to various platforms
- Machine learning for 2D image recognition problems
- Machine learning for video based recognition
- Machine learning for 3D data
Learning outcomes
After the course, the student can
- describe the most important application areas of machine learning in computer vision
- explain the structure of standard convolutional neural networks
- explain the key concepts of (convolutional) neural network training
- apply machine learning to image recognition using Python
Course material
- Lecture slides
- Recorded lecture videos
- Scientific papers
- Book: Computer vision: algorithms and applications, 2nd edition. Szeliski, Richard, 2022.
Teaching schedule
The course can be studied online except for the final exam. There are voluntary exercise and programming labs (20 h total) that are also live streamed. Teaching schedule can be found in Peppi closer to the course start.
Completion methods
Electronic exam completed in a Finnish university (on campus). Please familiarise yourself with the terms and conditions.
More information in the University of Vaasa study guide.
You can get a digital badge after completing this course.