CSE 312 Winter 2020

Lecture Topics

Lecture Topics

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Date | Description |
---|---|

January 6 |
Overview; Counting: product rule Reading: BT 1.1, 1.2, 1.6, Intro to Schnapsen, Rules of Schnapsen Notes, HW1 LaTeX source |

January 8 |
Counting: permutations Reading: Safety First (Schnapsen analysis) Trick mechanics, Notes |

January 10 |
Counting: combinations, complementing Notes |

January 13 |
Counting: binomial theorem, inclusion-exclusion, pigeonhole principle Reading: BT 1.3 Notes, HW2 LaTeX source |

January 15 |
Intro to probability, equally likely outcomes Notes |

January 17 |
Conditional probability Reading: BT 1.5 Notes |

January 22 |
Law of Total Probability, Bayes' Theorem Reading: BT 1.4 Notes, HW3 LaTeX source |

January 24 |
Independent events Reading: BT 2.1-2.3 Notes |

January 27 |
Gambler's Ruin, Naive Bayes classifier Reading: Naive Bayes notes Slides (PDF, PPTX), Notes |

January 29 |
Random variables, expectation Reading: BT 2.4, Expected Game Points, previous year's exercise Notes, HW4 LaTeX source |

January 31 |
Geometric random variable, linearity of expectation Reading: BT 2.7 Notes |

February 3 |
Variance, independent random variables Notes |

February 5 |
Uniform, Bernoulli, binomial distributions Reading: BT 3.1-3.2 Notes, Slide pack 6 slide 67 |

February 7 |
Error-correcting codes, Poisson distribution Slide pack 6 slides 68-80, general Hamming code, HW5 LaTeX source |

February 10 |
Continuous random variables Reading: BT 3.3 Notes |

February 14 |
Uniform, exponential distributions Reading: BT 7.4 Notes, HW6 LaTeX source |

February 19 |
Midterm debriefing, normal distribution Slide pack 7, slides 20-24, Notes |

February 21 |
Central Limit Theorem Slide pack 7, slides 24-33, Demo, Notes, HW7 LaTeX source |

February 24 |
Approximating binomial via Central Limit Theorem, continuity correction Reading: BT 7.1 Slide pack 10, slides 30-42 |

February 26 |
Markov and Chebyshev inequalities Reading: BT 7.2, 7.5 Notes |

February 28 |
Chernoff Inequality, law of large numbers Reading: Maximum likelihood estimators Slide pack 10, slides 14-19, Notes |

March 2 |
Maximum likelihood estimators Reading: Bias and confidence intervals Notes, HW8 LaTeX source |

March 4 |
Maximum likelihood estimators for normal distribution; bias Notes |

March 6 |
Confidence intervals Notes |

March 9 |
Probabilistic algorithms: primality, quicksort Notes |

March 11 |
Freivalds' algorithm Freivalds' algorithm slides |

March 13 |
Doktor Schnaps live: watch the Maestro get crushed! Where Doktor Schnaps implements randomization |