a1 - CSCD11 Machine Learning and Data Mining, Fall 2010...

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CSCD11 Machine Learning and Data Mining, Fall 2010 Assignment 1: Least-Squares Regression Due Friday, October 1, 5pm Note: For this assignment you will write two functions and one script in Matlab. You will hand in one tar-file containing these three files. Any questions below that ask for your thoughts or reasoning can be answered within the Matlab scipt as comments, much like the comments that appear in the tutorials and demos that we provide to you. First Steps Your goal in this assignment is to implement least-squares (LS) estimators for polynomial regression models. To get started, download the demo regression code that is discussed in the second week’s tutorial. The demo is available on the course calendar website (accessable from the course website). Also download the training data in a1TrainingData.mat from the same website. The demo shows a couple of ways in which one might compute the least-squares estimate of a linear model for a function that maps a scalar input to a scalar output. The last section of the demo shows the prefered method, with matrix-vector operations and the pseudo-inverse. Your first task is to read through and run this demo (cell by cell, reading the comments in each cell). Your next task is to look at the training data. Load the training data into Matlab. You will see that the
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This note was uploaded on 11/09/2010 for the course CS CSCD11 taught by Professor Davidfleet during the Spring '10 term at University of Toronto- Toronto.

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a1 - CSCD11 Machine Learning and Data Mining, Fall 2010...

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