Bayesian Reasoning and Machine Learning



The book begins with the basic concepts of graphical models and inference. For the independent reader chapters 1,2,3,4,5,9,10,13,14,15,16,17,21 and 23 would form a good introduction to probabilistic reasoning, modelling and Machine Learning. The material in chapters 19, 24, 25 and 28 is more advanced, with the remaining material being of more specialised interest. Note that in each chapter the level of material is of varying difficulty, typically with the more challenging material placed towards the end of each chapter. As an introduction to the area of probabilistic modelling, a course can be constructed from the material as indicated in the chart.

The material from parts I and II has been successfully used for courses on Graphical Models. I have also taught an introduction to Probabilistic Machine Learning using material largely from part III, as indicated.
These two courses can be taught separately and a useful approach would be to teach first the Graphical Models course, followed by a separate Probabilistic Machine Learning course.
A short course on approximate inference can be constructed from introductory material in part I and the more advanced material in part V, as indicated. The exact inference methods in part I can be covered relatively quickly with the material in part V considered in more in depth.
A timeseries course can be made by using primarily the material in part IV, possibly combined with material from part I for students that are unfamiliar with probabilistic modelling approaches. Some of this material, particularly in chapter 25 is more advanced and can be deferred until the end of the course, or considered for a more advanced course.
The references are generally to works at a level consistent with the book material and which are in the most part readily available.


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