This is a list of recommended books to accompany the lecture:
- Probabilistic Machine Learning: An Introduction, by Kevin P Murphy, 2022
- Available through the university library as an e-book and here.
- Probabilistic Machine Learning: Advanced Topics, by Kevin P Murphy, 2023
- Available here.
- Pattern Recognition and Machine Learning, by Christopher M Bishop, 2006
- Available in the university library and here.
- Deep Learning, by Christopher M Bishop and Hugh Bishop, 2024
- Available through university library as an e-book.
- 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.
- 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.
- 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.
- Patterns, Predictions, and Actions: A Story about Machine Learning, by Moritz Hardt and Benjamin Recht, 2022
- Available here.
- Dive into Deep Learning, by Aston Zhang, Zachary C Lipton, Mu Li, and Alexander J Smola, 2023
- Available here.
- Understanding Computational Bayesian Statistics, by William M Bolstad, 2009
- Available in the university library.
- Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016
- Available here.