Appendix A — Further study

In this module we can only touch the surface of the field of multivariate statistics and machine learning. If you would like to study further I recommend the following books below as a starting point.

A.1 Recommended reading

For multivariate statistics and machine learning:

• Izenman (2008) Modern Multivariate Statistical Techniques. Springer.
• Rogers and Girolami (2017) A first course in machine learning (2nd Edition). Chapman and Hall / CRC.
• James et al. (2021) An introduction to statistical learning with applications in R (2nd edition). Springer.
• James et al. (2023) An introduction to statistical learning with applications in Python. Springer.

A.2 Advanced reading

Additional (advanced) reference books for probabilistic machine learning are:

• Bishop (2006) Pattern recognition and machine learning. Springer.
• Murphy (2022) Probabilistic Machine Learning: An Introduction. MIT Press.
• Murphy (2023) Probabilistic Machine Learning: Advanced Topics. MIT Press.
• Prince (2023) Understanding Deep learning. MIT Press.

You can find further suggestions on my list of online textbooks in statistics and machine learning.

Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer. https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/.

Izenman, A. J. 2008. Modern Multivariate Statistical Techniques. New York: Springer. https://doi.org/10.1007/978-0-387-78189-1.

James, G., D. Witten, T. Hastie, and R. Tibshirani. 2021. An Introduction to Statistical Learning with Applications in R. 2nd ed. Springer. https://doi.org/10.1007/978-1-0716-1418-1.

James, G., D. Witten, T. Hastie, R. Tibshirani, and J. Taylor. 2023. An Introduction to Statistical Learning with Applications in Python. Springer. https://doi.org/10.1007/978-3-031-38747-0.

Murphy, K. P. 2022. Probabilistic Machine Learning: An Introduction. MIT Press. https://probml.github.io/pml-book/book1.html.

———. 2023. Probabilistic Machine Learning: Advanced Topic. MIT Press. https://probml.github.io/pml-book/book2.html.

Prince, S. J. D. 2023. Understanding Deep Learning. MIT Press. https://mitpress.mit.edu/9780262048644/understanding-deep-learning/.

Rogers, S., and M. Girolami. 2017. A First Course in Machine Learning. 2nd ed. Chapman; Hall / CRC. https://doi.org/10.1201/9781315382159.