My advice is not to try to read through these books (like Elements or Bishop's book).  If I were to learn the topic from scratch again * I would:<p>1. Learn basic terminology (basically, skim the chapters and understand roughly what the topics are)<p>2. Work on a problem in depth.  You are probably interested in a certain area or type of problem.<p>a. Read the relevant chapters in detail.<p>b. Pick up the necessary math along the way using additional references.  This way you are motivated to learn it (whether it be calculus, probability, or linear algebra).  E.g., it would be hard to approach McDiarmid's Inequality and be able to imagine its use.  However, if you run across it in a book/paper you'll understand the context.<p>c. Lastly, checkout recent NIPS, ICML, and JMLR papers on the topic (nips.cc, jmlr.org, and icml isn't centralized, but each conference can probably be found online).<p>* - I am a graduate student and have been studying statistical machine learning for the last 3 years.