R1 | Set Notation, Terms and Operators (include De Morgan's), Sample Spaces, Events, Probability Axioms and Probability Laws (PDF) | (PDF) |

R2 | Conditional Probability, Multiplication Rule, Total Probability Theorem, Baye's Rule (PDF) | (PDF) |

R3 | Introduction to Independence, Conditional Independence (PDF) | (PDF) |

R4 | Counting; Discrete Random Variables, PMFs, Expectations (PDF) | (PDF) |

R5 | Conditional Expectation, Examples (PDF) | (PDF) |

R6 | Multiple Discrete Random Variables, PMF (PDF) | (PDF) |

R7 | Continuous Random Variables, PMF, CDF (PDF) | (PDF) |

R8 | Marginal, Conditional Densities/Expected Values/Variances (PDF) | (PDF) |

R9 | Derivation of the PMF/CDF from CDF, Derivation of Distributions from Convolutions (Discrete and Continuous) (PDF) | (PDF) |

R10 | Transforms, Properties and Uses (PDF) | (PDF) |

R11 | Iterated Expectations, Random Sum of Random Variables (PDF) | (PDF) |

R12 | Expected Value and Variance (PDF) | (PDF) |

R13 | Recitation 13 | (PDF) |

R14 | Prediction; Covariance and Correlation (PDF) | (PDF) |

R15 | Weak Law of Large Numbers (PDF) | (PDF) |

R16 | Bernoulli Process, Split Bernoulli Process (PDF) | (PDF) |

R17 | Poisson Process, Concatenation of Disconnected Intervals (PDF) | (PDF) |

R18 | Competing Exponentials, Poisson Arrivals (PDF) | (PDF) |

R19 | Markov Chain, Recurrent State (PDF) | (PDF) |

R20 | Steady State Probabilities, Formulating a Markov Chain Model (PDF) | (PDF) |

R21 | Conditional Probabilities for a Birth-death Process (PDF) | (PDF) |

R22 | Central Limit Theorem (PDF) | (PDF) |

R23 | Last Recitation, Review Material Covered after Quiz 2 (Chapters 5-7) | |