Fundamental concepts of probability and reasoning under probabilistic models, with engineering applications.
The objective of the course is to introduce fundamental concepts of probability and reasoning under probabilistic models. Topics span the core curriculum of probability theory, from counting principles and basic axioms through random variables, expectation, and limit theorems.
| Week | Topic |
|---|---|
| Week 1 | Probability, Counting Principles |
| Week 2 | Sample Space, Events, Axioms of Probability |
| Week 3 | Equally Likely Outcomes |
| Week 4 | Conditional Probability, Independent Events, Bayes’ Formula |
| Week 5 | Random Variables, Probability Mass Function |
| Week 6 | Expected Value and Variance — First Midterm |
| Week 7 | Discrete Random Variable Examples |
| Week 8 | More Examples on Discrete Random Variables |
| Week 9 | Continuous Random Variables, PDF, Expectation, Variance |
| Week 10 | Examples of Continuous Random Variables |
| Week 11 | Jointly Distributed Random Variables, Independence, Sums |
| Week 12 | Second Midterm — Conditional Distributions |
| Week 13 | Covariance, Variance of Sums, Correlation |
| Week 14 | Properties of Expectation, Conditional Expectation |
| Week 15 | Inequalities and Limit Theorems |