External resources

• Kevin Murphy: https://probml.github.io/pml-book/book1.html

• David MacKay: http://inference.org.uk/mackay/itila/

• David Barber: http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online

• Chris Bishop: https://microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

• @Cmrn_DP https://ptgmedia.pearsoncmg.com/images/9780133902839/samplepages/9780133902839.pdf

• @rlmcelreath https://xcelab.net/rm/statistical-rethinking/

• @willkurt https://nostarch.com/learnbayes

• @avehtari et al. http://stat.columbia.edu/~gelman/book/ (with course materials).

• Michael Betancourt https://betanalpha.github.io/writing/

• Code/book reproduction of @rlmcelreath awesome Statistical Rethinking - https://bookdown.org/content/3686/

• https://gwthomas.github.io/docs/math4ml.pdf

• https://ai.stanford.edu/~gwthomas/notes/

• https://d2l.ai/index.html

• https://www.amazon.com/dp/3030403432/ - Linear Algebra and Optimization for Machine Learning: A Textbook

• http://mlwiki.org/index.php/Main_Page

• https://medium.com/towards-artificial-intelligence/basic-linear-algebra-for-deep-learning-and-machine-learning-ml-python-tutorial-444e23db3e9e#60ee

• https://machinelearningmastery.com/books-on-optimization-for-machine-learning/

Three linear algebra books - https://cosmathclub.files.wordpress.com/2014/10/georgi-shilov-linear-algebra4.pdf - https://math.byu.edu/~klkuttle/Linearalgebra.pdf - https://www.math.brown.edu/streil/papers/LADW/LADW_2017-09-04.pdf