Unit I: Probability Models And Discrete Random Variables

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This unit covers the basic framework of probability theory: probabilistic models, conditional probabilities, independence, the Bayes' rule, and counting methods. In addition, it introduces discrete random variables and the concept of the Probability Mass Function (PMF) used to describe the probability distribution of one or several random variables. Finally, it defines the concepts of expectation and variance, and their basic properties.

Lecture_1.jpg Lecture 1: Probability Models and Axioms

Lecture_2.jpg Lecture 2: Conditioning and Bayes' Rule

Lecture3.jpgLecture 3: Independence

Lecture4.jpgLecture 4: Counting

Lecture_5.jpg Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations

Lecture_6.jpg Lecture 6: Discrete Random Variable Examples; Joint PMFs

Lecture_7.jpg Lecture 7: Multiple Discrete Random Variables

Quiz_1.jpg Quiz 1


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