Textbooks

Recommended textbooks: The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.

The weekly plans will include reading suggestions from the above textbooks. Additional textbooks:

General learning book on statistical analysis:

  • Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer

  • Peter Hoff, A first course in Bayesian statistical models, Springer

General Machine Learning Books:

  • Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press

  • David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press

  • David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press