schedule-sandbox

Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|

Week 1: Jan 21, 23 | Overview of course, kinds of machine learning, python, jupyter notebooks, and mathematics of machine learning. | 01 Course Overview 01 High-D Spaces 02 Matrices and Plotting | From Python to Numpy, Chapters 1 - 2 Scipy Lectures, Section 1 Deep Learning, Chapters 1 - 5.1.4 | |

Week 2: Jan 28, 30 | Fitting linear models to data as a direct matrix calculation, and incrementally using stochastic gradient descent (SGD) | 03 Linear Regression | Deep Learning, Section 5.1.4 and 5.9 |

Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|

Week 7: Mar 3, 5 | Classification. LDA and QDA. K-Nearest Neighbors. | 12 Introduction to Classification 13 Gaussian Distributions | Jupyter Lab: Evolution of the Jupyter Notebook by Parul Pandey | |

Week 8: Mar 10, 12 | Classification with Neural Networks | 14 Classification with Linear Logistic Regression 15 Classification with Nonlinear Logistic Regression Using Neural Networks | A3 Neural Network Regression and Activation Functions due Friday March 15, 10:00 PM. | |

Mar 16 - 20 | Spring Break | |||

Week 9: Mar 24, 26 | Pytorch. | 16 Introduction to Pytorch |

Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|

Week 15: May 5 - May 7 | Ensembles. Other topics. | 32 Ensembles of Convolutional Neural Networks 33 Machine Learning for Brain-Computer Interfaces 34 Modeling Global Climate Change | ||

May 11 - 15 | Final Exams | Final Project Report due Tuesday, May 14, 10:00 PM. Here are is a links to most of the project reports |

schedule-sandbox.txt · Last modified: 2020/01/14 15:33 (external edit)

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