Ask HN: What maths are critical to pursuing ML/AI?
You absolutely need a solid grounding in multi-variable calculus, linear algebra, probability theory and information theory.  It will also be helpful to be well versed in graph theory.<p>In my opinion one of the best starting points is "Information Theory, Inference and Learning Algorithms" by David MacKaye.  It's a bit long in the tooth now, but it is still one of the most approachable and well written books in the field.<p>Another old book that stands up very well is "Probability Theory: the Logic of Science" by E. T. Jaynes.<p>"Elements of Statistical Learning" by Tibshirani is also good.<p>"Bayesian Data Analysis" by Andrew Gelman is another great read.<p>"Deep Learning" by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances in that field.