• Basics of Bayesian statistics and recursive estimation theory
• Describe and model common sensors, and their measurements
• Compare typical motion models used for positioning, in order to know when to use them in practical problems
• Describe the essential properties of the Kalman filter (KF) and apply it on linear state space models
• Implement key nonlinear filters in Matlab, in order to solve problems with nonlinear motion and/or sensor models
• Select a suitable filter method by analysing the properties and requirements in an application

Section 1 - Introduction and Primer in statistics
Section 2 - Bayesian statistics (Week 1)
Section 3 - State space models and optimal filters (Week 1)
Section 4 - The Kalman filter and its properties (Week 2-3)
Section 5 - Motion and measurements models (Week 2-3)
Section 6 - Non-linear filtering (Week 4)
Section 7 - Particle filter (Week 5)

I found the course very useful and also very exciting. The content and structure of the course was very well planned. The way the concepts were explained was also very understandable and engaging. I learnt a lot during the course and will be using it a lot in my own work. I look forward to similar courses being offered in future.