Mathematical and Statistical Foundations for Analytics

Description

This course consists of two parts: (i) The first half of the course focuses on probabilistic and statistical foundations of data analytics. At the end of this part, students will have the mathematical knowledge in following topics: probabilities, random variables, the Central Limit Theorem; prior and posterior distributions, and Bayes’ rule; correlation, and Sampling. (ii) The second half of the course focuses on mathematical foundations of decision analytics. At the end of this part, students will have the mathematical knowledge in following topics: linear algebra; calculus of several variables; convexity; separating hyperplanes; unconstrained and constrained optimization; lagrange multipliers.