Expander Graph Application 2: Derandomization || @ CMU || Lecture 16c of CS Theory Toolkit

How fully-explicit expander graphs can be used to 'magically' decrease the error of a randomized algorithm while not increasing the number of random bits used (or hardly increasing it) . Lecture 16c of "CS Theory Toolkit": a semester-long graduate course on math and CS fundamentals for research in theoretical computer science, taught at Carnegie Mellon University.

Resources for this lecture:
"Expander graphs and their applications", by Hoory, Linial, and Wigderson.