Random Numbers & Monte Carlo Methods

CSE Lecturer Candidate Seminar
In this sample lecture I'll cover the basics of using random numbers and Monte Carlo methods to design probabilistic algorithms. We will identify what probabilistic algorithms are and how they can be used to solve hard problems, review the built-in Python pseudo-random number system and use it to simulate random chance, and apply Monte Carlo methods to several coding examples.
Kelly Rivers is a PhD candidate at Carnegie Mellon University in the Human-Computer Interaction Institute, where she is advised by Ken Koedinger. She specializes in teaching CS0 and CS1 courses at large scale, and tries to incorporate her research into her classes. This research focuses on developing data-driven methods for generating hints and feedback for students who are learning how to code, and draws inspiration from the fields of intelligent tutoring systems, program transformations, and learning science theory. Kelly graduated from Carnegie Mellon with a B.S. in Mathematics and Computer Science in 2011 and plans to defend her thesis in the summer of 2017.