Provides a solid foundation to concepts of Probability Theory, Random Processes and Statistics required for Engineers designing and using AI. The course starts with axiomatic definition of probability metric and uses this to build the foundation on conditional probability, Baye's Theorem, and definition of probability density and distribution functions of discrete and continuous random variables. This foundation is then used to understand analyses of functions of one or more random variables, define moments and conditional moments of random variables and apply these concepts to parameter estimation and prediction. The class will emphasize the importance and wide applicability of Gaussian random variables. Hypothesis testing and its applicability to Artificial Intelligence will be explored. The class concludes with fundamentals concepts of Random Processes and use of Markov Chains for analyzing state transitions.