Obv you would want to talk to whoever your advisor is or a professor in the ML department at your school. Otherwise, find existing books/courses and see what yo

Obv you would want to talk to whoever your advisor is or a professor in the ML department at your school. Otherwise, find existing books/courses and see what you'll need for background such as this free book: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ http://www.cs.cmu.edu/~mgormley/courses/10701-f16/schedule.html http://www.cs.cmu.edu/~mgormley/courses/10701-f16/schedule.h... (general grad level intro) http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/ http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/ (in depth grad level intro, both include a youtube lecture playlist). The requirements listed here http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/faq.html http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/faq... -Basic probability and statistics are a plus. -Basic linear algebra (matrices, vectors, eigenvalues) is a plus. Knowing functional analysis would be great but not required. -Ability to write code that exceeds 'Hello World’. Preferably beyond Matlab or R. -Basic knowledge of optimization. Having attended a convex optimization class would be great but the recitations will cover this.