I'd recommend the slides from Pedro Domingos' (UW) ML class. They give a broad overview of the ML landscape. He follows Tom Mitchell's (CMU) book pretty closel

I'd recommend the slides from Pedro Domingos' (UW) ML class. They give a broad overview of the ML landscape. He follows Tom Mitchell's (CMU) book pretty closely. http://www.cs.washington.edu/education/courses/cse546/10wi/ http://www.cs.washington.edu/education/courses/cse546/10wi/ Both the slides and the book are highly readable. There are no good papers that cover everything. I wouldn't necessarily dive in to a paper on regularization or non-parametrics for a beginning class (is this a slug talk series?). The Netflix paper really doesn't convey good understanding of ML (they combine a large number of techniques at the problem) but you might want to cover boosting using a different source.