This first course in probability will take a classical approach, following the classic text by Will Feller, An Introduction to Probability Theory and its Applications. In particular, the topics will include: combinatorial analysis; combination of events, conditional probabilities, and independence; analysis of fluctuation; standard probability distributions (including binomial, normal, and Poisson); the law of large numbers and the central limit theorem; Markov chains; and random walks. The course will not cover measure theory or formal proofs, but there will be proofs at an appropriate level of rigor. The class should be of interest for both theoretical and applied purposes. The class will be a prerequisite for Machine Learning in Spring 2021.Delivery Method: Entirely remote (synchronous)
Prerequisites: MAT 4133 Calculus A, or equivalent, or permission of the instructor. For registration, email Andrew McIntyre at firstname.lastname@example.org once 4000-level registration is open.
Course Level: 4000-level
T/F 8:30AM-12:10PM (1st seven weeks)
Maximum Enrollment: 20
Course Frequency: Every 2-3 years
Categories: All courses , Updates , Computer Science , Mathematics
Tags: economics , computer programming , quantitative