Earlier quoted context omitted.
ah ok. I am also starting with ML, so take this with a grain of salt. While the courses i listed are required for most CS majors, i think they are more important for ML than many other fields of CS (of course there are exceptions). I think a deeper understanding of LA, Calculus and Numeric compared to normal CS-majors is beneficial (but if you majored in math you probably had to take these in more depth than most CS-…
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 th…
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.