# Math637 – Spring 2020

Time & Place: MWF 12:20PM – 1:10PM, Room: EWG 204 (Ewing Hall)

Syllabus: pdf version

Textbook: T. Hastie, R. Tibshirani, J. Friedman, *The Elements of Statistical Learning: Data Mining,
Inference, and Prediction*, Springer, 2009.

Available for free at http://statweb.stanford.edu/~tibs/ElemStatLearn/.

Lectures:

Date | Topic | Slides |

February 10 | Overview | |

February 12 | Linear Regression: old and new | |

February 17 | Linear Regression: old and new (part 2) | |

February 24 | Consistency of Linear Regression | |

February 26 | Subset selection and Coefficients Penalization | |

March 2 | Model selection | |

March 4 | Computing the lasso solution | |

March 9 | Categorical data | |

March 11 | Introduction to statistical decision theory | |

March 30, April 1 | Logistic regression & Linear Discriminant Analysis | |

April 6 | Support vector machines | |

April 10 | Support vector machines and kernels | |

April 13 | Principal component analysis | |

April 15 | Decision trees | |

April 17 | Lab | |

April 20 | Random forest | |

April 22 | Neural networks I | |

April 24 | Neural networks II | |

April 27 | Neural networks Lab | |

April 29, May 1 | The singular value decomposition | |

May 4, 6 | Clustering I | |

May 8 | Clustering II | |

May 11 | Clustering Lab | |

May 13 | The EM algorithm | |

May 15 | Independent component analysis | |

May 21 | Project presentations | schedule |