supremus_58

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

this is the math necessary for most comp-sci majors. specifically to machine learning, which topics in mathematics are most relative to be pursued? what pathway should a math major take to have the most relative knowledge to pursue ML

sorry that the question came out so ambiguous. yes, by school i mean uni. the question may have better been asked as "what is the best route to take for a mathematics student to learn the most relevant ideas in ML?" i've take all the classes which you've listed, i dropped school somewhere halfway through. I know how to write code for ML, this isn't totally new to me.(by this i mean i know how to use the python ml library) i ask topically, what math is most important in the further development of ai?(ie complex analysis, combinatorics, graph theory, stochastic methods are all things important in ML, what other math is worth noting?) the math you listed are simply things required for most comp-sci majors.

wouldn't poverty largely be a product of inequality?

does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?