The main thing to understand is the basics. For example element wise multiplication vs full blown matrix operations. Think something like this: http://en.wikipe

The main thing to understand is the basics. For example element wise multiplication vs full blown matrix operations. Think something like this: http://en.wikipedia.org/wiki/Matrix_multiplication http://en.wikipedia.org/wiki/Matrix_multiplication Matlab/Octave is a great way to practice this due to the native data types. If python is your thing numpy's arrays are also pretty easy to digest. Subtle little tricks like this: https://www.youtube.com/watch?v=evF-3ykjRU0 https://www.youtube.com/watch?v=evF-3ykjRU0 And understanding the dynamics of scalar operations vs matrix - vector operations. The machine learning class has some good fundamentals if you need a refresher on how something works. There will be more complex things like some optimization algorithms have different uses for eigen values: http://see.stanford.edu/materials/lsocoee364b/11-conj_grad_slides.pdf http://see.stanford.edu/materials/lsocoee364b/11-conj_grad_s... See: http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors http://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors One other thing might be understanding different ways you can manipulate data. In this case, numerical representation here is an example per row when I toss in one matrix for training. This is applicable to many machine learning problems.