Literature

The course literature consists only of material that is freely available online.
There are two book which covers most of the course:

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.

However, we will be closer to Bishop in terms of notation and disposition.  If you want to pick only one, we therefore recommend Bishop.
Detailed reading suggestions are found under each lecture.

In addition to those books, we will also make use of:

• Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön, Machine Learning - A First Course for Engineers and Scientists Links to an external site., 2021.
• Diederik Kingma and Max Welling, An introduction to Variational Autoencoders Links to an external site., 2019.

We also provide a formula sheet for the Gaussian distribution Download formula sheet for the Gaussian distribution which summarizes all results about the Gaussian distribution needed in the course.

Recommended supplementary reading

If you are looking for additional/complementary reading for the course, some suggestions are:

• Kevin P. Murphy. Machine learning - a probabilistic perspective (available as e-book via the library), MIT Press, 2012. Similarly to the comprehensive books by Barber and Bishop, also this book covers a lot of interesting material on probabilistic machine learning.
• Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning (freely available), to be published by Cambridge University Press. A very recent book with focus on the mathematical tools for modern machine learning.
• Simon Rogers and Mark Girolami. A first course on machine learning (available as e-book via the library), CRC Press 2017. A modern book with a pedagogigal take on probabilistic machine learning, although not covering all topics in this course.