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.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.Another old book that stands up very well is "Probability Theory: the Logic of Science" by E. T. Jaynes."Elements of Statistical Learning" by Tibshirani is also good."Bayesian Data Analysis" by Andrew Gelman is another great read."Deep Learning" by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances in that field.