1. Introduction to Probabilistic Robotics; Basics of Probability; Bayes filter (last changed: 22/11/2017) [R.M.]
  2. Basics of Linear methods for Regression; Kalman filter and applications [R.M.]
  3. Nonparametric filters; Particle filter [R.M.]
  4. Planning and Control: Markov Decision Processes [A.F]
  5. Exploration and information gathering [A.F.]
  6. Plan monitoring for robotics; Applications for mobile robots [A.F.]