This course prepares students with the fundamental principles and methods in probabilities and statistics for machine learning and data sciences. It covers from first a review of basic concepts such as common distributions, conditional and joint distributions, covariance and correlation, central limit theorem, sampling, to classical theory in estimation, hypothesis testing and Bayesian inference, and some advanced topics for regression, clustering, classification and learning.

CILO-1: Apply knowledge of probabilities and statistics and analyze their applications in machine learning and data sciences. CILO-2: Analyze and apply knowledge of statistical learning such as estimation, Bayesian inference, regression, clustering, classification and learning. CILO-3: Use computer softwares such as Matlab, R, MiniTab, or Python for simulations in probability and statistics.