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Schedule

Announcements

Feb 19: The links in A3 for nnA3.tar and A3grader.tar now work.

Lecture videos are available at this CS480 video recordings site.

Week	Topic	Material	Reading	Assignments
Week 1: Jan 17 - Jan 20	Overview. Intro to machine learning. Python.	01 Course Overview, 02 Matrices and Plotting,	The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016. Section 1 of Scipy Lecture Notes
Week 2: Jan 23 - Jan 27	Probability distributions and regression.	03 Linear Regression, 04 Gaussian Distributions

Week	Topic	Material	Assignments
Week 3: Jan 30 - Feb 3	Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression.	05 Fitting Gaussians, 06 Probabilistic Linear Regression, 07 Linear Ridge Regression and Data Partitioning, 08 Sample-by-Sample Linear Regression	A1 Linear Regression due Monday, January 30th at 10:00 PM.
Week 4: Feb 6 - Feb 10	Regression with fixed nonlinearities. Nonlinear regression with neural networks. Feb 10: Guest Speaker Mike Morain, Machine Learning at Amazon, UK.	09 Linear Regression with Fixed Nonlinear Features, 10 Nonlinear Regression with Neural Networks
Week 5: Feb 13 - Feb 17	Neural Networks	10 Nonlinear Regression with Neural Networks, 11 More Nonlinear Regression with Neural Networks	A2 Ridge Regression with K-Fold Cross-Validation due Monday, February 13th at 10:00 PM. Here are examples of good A2 reports.
Week 6: Feb 20 - Feb 24	Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee.	12 Autoencoder Neural Networks
Week 7: Feb 27 - Mar 3	Recurrent Neural Networks.	13 Recurrent Neural Networks 14 Introduction to Classification	A3 Neural Network Regression due Wednesday, March 1st at 10:00 PM.