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Lecture videos are available at this CS445 video recordings site.

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

Week 1: Jan 16 - Jan 19 | Overview. Intro to machine learning. Python. | 01 Course Overview 02 Matrices and Plotting | From Python to Numpy, Chapters 1 - 2 Deep Learning, Chapters 1 - 5.1.4 | |

Week 2: Jan 22 - Jan 26 | Fitting linear models to data as a direct matrix calculation, and incrementally using stochastic gradient descent (SGD) | 03 Linear Regression 04 Linear Regression Using Stochastic Gradient Descent (SGD) | ||

Week 3: Jan 29 - Feb 2 | Ridge regression. Data partitioning. Probabilistic Linear Regression. Regression with fixed nonlinearities. | 05 Linear Ridge Regression and Data Partitioning 06 Probabilistic Linear Regression 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. | A1 Linear Regression due Wednesday, January 31, 10:00 PM. Here are some good solutions. |

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

Week 4: Feb 5 - Feb 9 | Introduction to nonlinear regression with neural networks. | 08 Stochastic Gradient Descent with Parameterized Activation Function 09 Scaled Conjugate Gradient for Training Neural Networks | Deep Learning, Chapter 6 (skip 6.2) | |

Week 5: Feb 12 - Feb 16 | Lectures on Feb 12th and 14th are canceled. Friday, more neural networks | 10 More Nonlinear Regression with Neural Networks | ||

Week 6: Feb 19 - Feb 23 | Autoencoders. Activation functions. | 11 Autoencoder Neural Networks | Searching for Activation Functions, by Ramachandran, Zoph, and Le | A2 Neural Network Regression due Tuesday, February 20, 10:00 PM |

Week 7: Feb 26 - Mar 2 | Classification. LDA and QDA. K-Nearest Neighbors. | 12 Introduction to Classification (qdalda.py updated March 20) 13 Gaussian Distributions | A3 Activation Functions due Thursday, March 1, 10:00 PM |

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

Week 8: Mar 5 - Mar 9 | Classification with Neural Networks | 14 Classification with Linear Logistic Regression 15 Classification with Nonlinear Logistic Regression Using Neural Networks (updated March 18) 16 Introduction to Pytorch | ||

Mar 12 - Mar 16 | Spring Break | |||

Week 9: Mar 19 - Mar 23 | Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits. | 17 Analysis of Neural Network Classifiers and Bottleneck Networks (updated March 19, 10:20 AM) 18 Dealing with Time Series by Time-Embedding 19 Recurrent Neural Networks | ||

Week 10: Mar 26 - Mar 30 | Convolutional Neural Networks | 20 Classifying Hand-drawn Digits 21 Convolutional Neural Networks 22 Introduction to Reinforcement Learning | Reinforcement Learning: An Introduction, by Sutton and Barto | A4 Classification with QDA, LDA, and Logistic Regression (use() return value updated March 20) due Tuesday, March 27, 10:00 PM |

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

Week 11: Apr 2 - Apr 6 | Reinforcement Learning. Games using Tabular Q functions. | 23 Reinforcement Learning with Neural Network as Q Function | Project proposal due at 10 pm Friday evening. You are welcome to start with a copy of the linked Google Doc. | |

Week 12: Apr 9 - Apr 13 | Reinforcement Learning using Neural Networks as Q functions. | 24 Reinforcement Learning to Control a Marble 25 Reinforcement Learning for Two Player Games | ||

Week 13: Apr 16 - Apr 20 | Unsupervised Learning. Dimensionality Reduction. Clustering. | 26 Linear Dimensionality Reduction 27 Examples of Linear Dimensionality Reduction 28 K-Means Clustering | ||

Week 14: Apr 23 - Apr 27 | Support Vector Machines. | 29 Hierarchical Clustering | A5 Control a Marble with Reinforcement Learning due Monday, April 23rd, 10:00 PM |

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

Week 15: Apr 30 - May 4 | Ensembles. Other topics. | |||

May 7 - May 10 | Final Exams | Final Project Report due Wednesday, May 9, 10:00 PM |

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