I'm looking for a compact set of notes/slides that state the most important definitions and results in probability theory and stochastic processes. In particular, the target audience is master's students in engineering/CS that don't have a background in measure theory and should serve as a refresher at the beginning of a course that in part covers stochastic optimization algorithms.
Edit: Everything that is needed for the course can be found in "Probability and Random Processes" by Grimmett and Stirzaker, so it seems like I'll just need to write up a cheat-sheet-like document based on that book unless something similar already exists.
