Videos
- Chapter 1: Basic Concepts
- All videos for chapter 1
- 1.1.Introduction to Probability
- 1.2.Sets, Functions
- 1.3.Probability Axioms
- 1.4.Discrete and Continuous Probability Models
- 1.5.Conditional Probability
- 1.6.Independent Events - Part 1
- 1.7.Independent Events - Part 2
- 1.8.Law of Total Probability
- 1.9.Bayes Theorem
- 1.10.Boy or Girl Paradox
- Chapter 2: Combinatorics: Counting and Methods
- Chapter 3: Discrete Random Variables
- Chapter 3:Discrete Random Variables (All videos)
- 3.1.Introduction to Random Variables: Discrete Random Variables - Part 1
- 3.2.Discrete Random Variables, PMF, Independent Random Variables
- 3.3.Geometric and Binomial Random Variables
- 3.4.Poisson, Pascal, and Hypergeometric Distributions
- 3.5.CDF for Discrete Random Variables
- 3.6.Expectation of Discrete Random Variables
- 3.7.Linearity of Expectation
- 3.8.Functions of Discrete Random Variables
- 3.9.Variance and Standard Deviation
- Chapter 4: Continuous Random Variables
- Chapter 4:Continuous Random Variables (All videos)
- 4.1.Continuous Random Variables
- 4.2.Probability Density Function (PDF) for Continuous Random Variables
- 4.3.Expected Values for Continuous Random Variables
- 4.4.Variance for Continuous Random Variables
- 4.5.Functions of Continuous Random Variables - Part 1
- 4.6.Functions of Continuous Random Variables - Part 2
- 4.7.Uniform Distribution
- 4.8.Exponential Random Variable
- 4.9.Normal (Gaussian) Distribution
- 4.10.Mixed Random Variables
- 4.11.Delta Function
- 4.12.Mixed Random Variables Using the Delta Function
- Chapter 5: Joint Distributions
Note: More detailed videos covering all chapters of the book are available through the online courses.
Brief Introduction to Machine Learning (No Coding)