I'm a student at the University of Sydney focused primarily on Natural Language Processing and Machine Learning. My largest regret is not doing more math and th

I'm a student at the University of Sydney focused primarily on Natural Language Processing and Machine Learning. My largest regret is not doing more math and that's something I plan on fixing after Honours (either in PhD or in my spare time wherever I end up). If you can, take as much math as possible. I'd also mention that although the traditionally important math for computer science is discrete mathematics and graph theory you really can't underestimate how important linear algebra, differential calculus and probability theory are if you want to actually understand the theory behind a lot of machine learning algorithms. If you're looking for a fun project to do that involves mathematics I'd highly suggest writing a raytracer. The amount of linear algebra that you end up learning accidentally just trying to render a sphere with lighting is amazing and it really inspires you to learn more. I would also highly recommend going through Andrew Ng's AI course from Stanford[1] where he derives many common machine learning algorithms in a simple to follow manner. It shows just how many wonderful bits and pieces of math fall into the area. He only assumes a basic knowledge of probability theory and linear algebra and takes great strides in ensuring there's no "magic steps" in the derivations. If you get lost, pause the video and read the (wonderfully written) lecture notes or Google for the particular topic you're stuck on. Review notes are also provided for Linear Algebra, Probability Theory, Convex Optimization, Hidden Markov Models and Gaussian Processes if any of those really catch your fancy, but they're not required reading to necessarily understand the majority of the course. [1] http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a...