Books#

This is a list of recommended books to accompany the lecture:

  1. Probabilistic Machine Learning: An Introduction, by Kevin P Murphy, 2022

    • Available through the university library as an e-book and here.

  2. Probabilistic Machine Learning: Advanced Topics, by Kevin P Murphy, 2023

  3. Pattern Recognition and Machine Learning, by Christopher M Bishop, 2006

    • Available in the university library and here.

  4. Deep Learning, by Christopher M Bishop and Hugh Bishop, 2024

    • Available through university library as an e-book.

  5. Gaussian Processes for Machine Learning, by Carl E Rasmusses and Christopher KI Williams, 2006

    • Available through the university library as an e-book and here.

  6. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, by Steven L Brunton and J Nathan Kutz, 2019

    • Available through the university library as an e-book and further materials here.

  7. An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, 2021

    • Available through the university library as an e-book and here.

  8. Patterns, Predictions, and Actions: A Story about Machine Learning, by Moritz Hardt and Benjamin Recht, 2022

  9. Dive into Deep Learning, by Aston Zhang, Zachary C Lipton, Mu Li, and Alexander J Smola, 2023

  10. Understanding Computational Bayesian Statistics, by William M Bolstad, 2009

    • Available in the university library.

  11. Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016