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Abstract
The course will cover: Random experiments, sample spaces, events| Probability measures| Conditional probability, Bayes rule.| Independence| Random variables (discrete & continuous) and their distributionfunction, Well-known distributions| Joint distributions| Expectation and variance| Covariance and correlation| Conditional expectation,Modes of convergence.
Objective
The course will introduce the foundations of learning and making predictions from data. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.