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 &quot;Information Theory, Inference and Learning Algorithms&quot; by David MacKaye.  It&#x27;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 &quot;Probability Theory: the Logic of Science&quot; by E. T. Jaynes.<p>&quot;Elements of Statistical Learning&quot; by Tibshirani is also good.<p>&quot;Bayesian Data Analysis&quot; by Andrew Gelman is another great read.<p>&quot;Deep Learning&quot; by Ian Goodfellow and Yoshua Bengio is useful for getting caught up with recent advances in that field.