Machine Learning (UG)
Machine Vision
Mathematical Methods
for Computer Vision
Machine Learning (PG)
Principles of
Computer Vision
Advanced Computer Vision
An introduction to machine learning for third year undergraduate students. The course is an introduction to machine/computer vision for undergraduates. It covers the basics of image formation and human vision, linear and non-linear image operators, edge detection, motion analysis and classification, stereo reconstruction. This is one of the core module of the MSc in Computer Vision graduate course. It is designed to introduce students to the necessary mathematical tools to tackle computer vision problems, broadly covering four fields: calculus, linear algebra, probability and statistics, optimisation. This course's aim is to teach students the basics of machine learning (to be applied to their scenario of choice in P00408), including: clustering, linear SVMs, statistical learning, etcetera. An introduction to the principles of computer vision for PG students. This is the final module of the MSc in Vision course. The module is structured as a series of four seminars centered around four real-world application scenarios: surveillance, gaming and entertainment, robotics and navigation, human-robot interaction.