In this class, students will learn how to model, quantify, and analyze uncertainty. The fundamental tools of probability will be covered which are essential to analyze and make sense of data. The course will focus on introducing basic concepts and methodologies, and will contain multiple examples and real-world applications.

In the course, we cover the following topics:

• Axioms of probability
• Conditional probability, Independence, Conditional independence
• Bayes’ Rule, Bayesian inference
• Expectation, Variance, and Covariance
• Discrete/Continous random variables
• Probability mass/distribution functions
• Joint distributions
• Central limit theorem