Lecture # | Topic | Slides |
---|---|---|

1 | BST Permutations | |

2 | Combinations | |

3 | Introduction to Probability | |

4 | Conditional Probability and Bayes Theorem | |

5 | More on Conditional Probability and Independence | |

6 | Random Variables and Expectation | |

7 | Variance and Binomial Distribution | |

8 | Poisson and Other Discrete Distributions | |

9 | Continuous Distributions | |

10 | Normal and Exponential Distributions | |

11 | Joint Distributions | |

12 | Independent Random Variables | |

13 | Sums of Independent Random Variables and Conditional Distributions | |

14 | Properties of Expectation and QuickSort Analysis | |

15 | Covariance | |

16 | Correlation and Conditional Expectation | |

17 | Moment Generating Functions | |

18 | Helpful Inequalities and Bounds | |

19 | Law of Large Numbers and Central Limit Theorem | |

20 | Parameter Estimation and Method of Moments | |

21 | Maximum Likelihood Estimation | |

22 | Bayesian Estimation | |

23 | Machine Learning and Naive Bayes Classifier | |

24 | Logistic Regression | |

25 | Modeling Uncertainty and Utility | |

26 | Generating probabilities and Monte Carlo simulation | |

27 | Final Exam Review Examples |