Lecture 1: Introduction / What is CCV?
Lecture 2: Generative and discriminative models
Lecture 3: Graphical models / what are they?
Lecture 4: Graphical models / Examples of different sorts and "family of models"
Lecture 5: Probability thoery reminder, Bayes rule and Bayesian networks
Lecture 6: Inference in Bayesian networks
Lecture 7: Discrete HMMs
Lecture 8: Gaussian Mixtures and continuous valued HMMs
Lecture 9: Behaviour recognition: Bottom up/top-down vision. HIVIS, DBNs and DDNs, BAT
Lecture 10: Visual task control: ActIPret
Lecture 11: Learning in HMMs (discrete case)
Lecture 12: Learning in HMM (continuous case) & stochastic sampling
Lecture 13: Learning in BBNs, taxonomy of learning methods and 1 method in detail  
            (full observability and known structure)
Lecture 14: Learning in BBNs, overview of other 3 methods
Lecture 15: Active cameras, Bayes Nets and tasks - future challenges