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.
For multivariate statistics and machine learning:
- Härdle and Simar (2015) Applied multivariate statistical analysis. 4th edition. Springer.
- Marden (2015) Multivariate Statistics: Old School
- 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.
Additional (advanced) reference books for probabilistic machine learning are:
Bishop (2006) Pattern recognition and machine learning. Springer.
- Hastie, Tibshirani, and Friedman (2009) The elements of statistical learning: data mining, inference, and prediction. Springer.
- Murphy (2012) Machine learning: a probabilistic perspective. MIT Press.
You can find further suggestions on my list of online textbooks in statistics and machine learning.