About this Course
The course "Foundations of Probability and Random Variables" introduces fundamental concepts in probability and random variables, essential for understanding computational methods in computer science and data science. Through five comprehensive modules, learners will explore combinatorial analysis, probability, conditional probability, and both discrete and continuous random variables. By mastering these topics, students will gain the ability to solve complex problems involving uncertainty, design probabilistic models, and apply these concepts in fields like machine learning, AI, and algorithm design. What makes this course unique is its practical approach: students will develop hands-on proficiency in the R programming language, which is widely used in data science and statistical modeling. The course also includes real-world applications, allowing learners to bridge theoretical knowledge with practical problem-solving skills. Whether you are aiming to pursue advanced studies in machine learning or develop data-driven solutions in professional settings, this course provides the solid foundation you need to excel. Designed for learners with a background in calculus and basic programming, this course prepares you to tackle more advanced topics in computational science
What You'll Learn
- Master combinatorial techniques to solve counting and probability problems
- Apply probability axioms and construct Venn diagrams to evaluate probabilities
- Use Bayes' formula and conditional probability to solve real-world problems
- Analyze discrete and continuous random variables with appropriate functions
Prerequisites
- Basic familiarity with the topic and its common terminology
- Readiness to practice through applied exercises or case-based work
Instructors
Ian McCulloh
Tony Johnson