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Thanks, everyone, for a fun semester. I very much enjoyed reading your final reports. Follow this link to read the reports

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

Week 1: Jan 22 - Jan 25 | Overview. Intro to machine learning. Python. | 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 - Feb 1 | 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 3: Feb 4 - Feb 8 | Stochastic gradient descent (SGD). Ridge regression. Data partitioning. Probabilistic Linear Regression. Regression with fixed nonlinearities. | 04 Linear Regression Using Stochastic Gradient Descent (SGD) 05 Linear Ridge Regression and Data Partitioning 07 Linear Regression with Fixed Nonlinear Features | Deep Learning, Section 7.3 The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016. | |

Week 4: Feb 11 - Feb 15 | Introduction to nonlinear regression with neural networks. | 08 Stochastic Gradient Descent with Parameterized Activation Function | Deep Learning, Chapter 6 (skip 6.2) | A1 Stochastic Gradient Descent for Simple Models due Tuesday, February 12, 10:00 PM. Examples of good solutions |

Week 5: Feb 18 - Feb 22 | More neural networks | 09 Scaled Conjugate Gradient for Training Neural Networks 10 More Nonlinear Regression with Neural Networks | ||

Week 6: Feb 25 - Mar 1 | Autoencoders. Activation functions. | 11 Autoencoder Neural Networks | Searching for Activation Functions, by Ramachandran, Zoph, and Le Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks, by Eger, Youssef, and Gurevych | A2 Adam vs SGD due Tuesday February 26, 10:00 PM. |

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

Week 7: Mar 4 - Mar 8 | 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 11 - Mar 15 | 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 18 - Mar 22 | Spring Break | |||

Week 9: Mar 25 - Mar 29 | Pytorch. | 16 Introduction to Pytorch |

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

Week 15: May 6 - May 10 | Ensembles. Other topics. | 32 Ensembles of Convolutional Neural Networks 33 Machine Learning for Brain-Computer Interfaces 34 Modeling Global Climate Change | ||

May 13 - May 16 | Final Exams | Final Project Report due Tuesday, May 14, 10:00 PM. Here are is a links to most of the project reports |

start.txt · Last modified: 2019/06/03 13:46 by anderson

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