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Table of Contents

Schedule Spring 2020

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January

Week Topic Material Reading Assignments
Week 1:
Jan 21, 23
Overview of course, kinds of machine learning, python, jupyter notebooks. 01 Course Overview
01a Matrix Multplication on GPU
02 Matrices and Plotting
03 Linear Regression with SGD
Week 2:
Jan 28, 30
Supervised learning. Linear and nonlinear regression with artificial neural networks. 04 Linear Regression with Fixed Nonlinear Features
05 Introduction to Neural Networks

February

Week Topic Material Reading Assignments
Week 3:
Feb 4, 6
Gradient descent with Adam. Effects of network size and other parameters. 05 Introduction to Neural Networks
06.1 Gradient Descent with Adam
A1.1 Linear Regression with SGD due Thursday, Feb 6th, at 10:00 PM
Week 4:
Feb 11, 13
Optimizers class. NeuralNetwork class. Partioning data. 07.2 Optimizers, Data Partitioning, Finding Good Parameters HiPlot a new plotting library for visualizing results from multiple training experiments Exercises1. Do not check-in. Exercises will not be graded.
Week 5:
Feb 18, 20
Pytorch basics, loss functions, optimizers and nn module. 08 Pytorch autograd, nn.Module LaProp optimizer A2.5 Multilayer Neural Networks for Nonlinear Regression due Thursday, Feb 20th 10:00 PM
Week 6:
Feb 25, 27
Classification with generative models. LDA and QDA. 09 Introduction to Classification

March

Week Topic Material Reading Assignments
Week 7:
Mar 3, 5
Classification with linear logistic regression 10 Classification with Linear Logistic Regression The unreasonable effectiveness of deep learning in artificial intelligence A3.3 Cross-validation with Pytorch due Thursday, March 5th, 10:00PM
Week 8:
Mar 10, 12
Class inheritance. Nonlinear logistic regression with neural nets. 11 Code Reuse by Class Inheritance
12.1 Classification with Nonlinear Logistic Regression Using Neural Networks
Mar 16 - 20 Spring Break
Week 9:
Mar 24, 26
Start of online-only lectures. Thursday this week is first online class. No new material covered, but assignment questions can be discussed. Join the Microsoft Teams meeting using your firstname.lastname@colostate.edu login.

April

Week Topic Material Reading Assignments
Week 10:
Mar 31, Apr 2
Convolutional neural networks. Classification with Pytorch and Keras. 13 Convolutional Neural Networks A4.2 Classification of Hand-Drawn Digits due Thursday, April 2nd, 10:00PM
Week 11:
Apr 7, 9
Introduction to reinforcement learning, with discrete state and action using tables and neural networks.
Week 12:
Apr 14, 16
Reinforcement learning with continuous state and action.
Week 13:
Apr 21, 23
Decision Trees. Random Forests.
Week 14:
Apr 28, 30
Support Vector Machines. Ensembles.

May

Week Topic Material Reading Assignments
Week 15:
May 5, 7
Unsupervised learning. Clustering, K-Means, PCA, t-SNE.
May 11 - 15 Final Exam Week Final Project Report due Tuesday, May 12, 10:00 PM.
schedule.1585680097.txt.gz · Last modified: 2020/03/31 12:41 by 127.0.0.1