awesome, thank you! I took linear an analytic linear algebra class some years ago, but admittedly don't remember too much. I'm beginning to study more in depth matrix analysis, this and combinatorics are useful for understanding fundamentals of many algorithms. I've learned a little of Markov Decision Process, but i've been told by people working in the field that MDP isn't used much for neural net anymore, though this doesn't mean it shouldn't be learned. I'll definitely look into tensors.

here's a link i found looking into the subject: http://datascience.ibm.com/blog/the-mathematics-of-machine-l...

and here's some recommendations for math/ml texts i got in another thread that may be of use: https://news.ycombinator.com/item?id=13275031

i was also told that Markov-chains are not used for NN anymore, but i learn them because often they get combined with NNs and used for other aspects of the problem (if i remember right AlphaGo used them beside NNs).

the blog article was useful, thank you! A problem with most of the recommendations is that they are targeted at software engineers wanting to learn some ML. They mostly don't need to understand back propagation fully and don't have that complex of a problem. So the requirements are the absolut basics (what are vectors/matrices and how to multiply them). Also their math-background is either non-existent or a long time ago. I am a student and constantly learning math, so i am in a different position. I am also interested in really understanding NN, including recent papers. They vary, some being not very math-heavy (the dropout paper) and some being the opposite (bayes neural network, only skimmed some papers). Many paper introduce a new idea borrowed from other branches of math, so you can't specialise too much. A broad understanding is important. Also we still lack a unifying theory, so we definitely don't know what math will become really important in the future.