Instructor: Tina Kapur and Rajeev Surati
Focuses on modeling, quantification, and analysis of uncertainty by
teaching random variables, simple random processes and their
probability distributions, Markov processes, limit theorems, elements
of statistical inference, and decision making under uncertainty. This
course extends the discrete probability learned in the discrete math
class. It focuses on actual applications, and places little emphasis
on proofs. A problem set based on identifying tumors using MRI
(Magnetic Resonance Imaging) is done using Matlab.
Text:
Fundamentals of Applied Probability Theory, Al Drake.
Requirements: One exam, three assignments, two problem sets.
