CV: Computer Vision

Welcome to Computer Vision!

Computer Vision is the field of study that teaches computers how to 'see'. This means, how to go from the pixels in an image to the information that a human can describe when they see a picture, much like self-driving cars, autonomous robots, or social media apps that recommend images or videos based on your preferences. This course is an in-depth introduction to the field of Computer Vision.

The course is structured around different problems in computer vision, such as object recognition and video classification, and covers both classical and deep learning approaches.

The course can be taken without any prior knowledge of computer vision or deep learning, but it does assume some familiarity with machine learning concepts, and relevant mathematics and programming skills (see details under "Other Requirements"). The course delivers both theoretical and practical knowledge, and by the end you should be able to understand, design, and implement computer vision techniques for many real-world problems.

Learning Outcome

On completion of this course, the student will be able to:

1. Define and explain principles underpinning computer vision methods
2. Describe current vision problem settings and their current solutions
3. Implement, train and debug computer vision models
4. Design, explain, analyse, and compare the behaviour of computer vision models under different settings
5. Identify social and ethical implications of computer vision methods in the real world

Learning Activities

The course will be taught as a combination of:
- Live lectures.
- Tutorials to develop your ability to solve vision problems from a theoretical perspective.
- Lab sessions to develop practical skills. The coursework will be structured as a series of small non-assessed practice sessions, which will build up the skills for the assessed mini-project at the end of the course.