Coursework Assignment 1  Apply Pseudoinverse MSE Method to Fisher's Iris data  There are three different types of iris contained;"Setosa", "Versicolor" & "Virginica"  Using only 2 types of iris and use only petal length and width as the 2 features  Classify data into the two classes (Versicolor &Virginica)02 3 4 5 6 7 0.5 1 1.5 2 2.5 3 Petal Length Petal Width Fisher's Iris Data for 2 Classes Only 2 Virginica VersicolorCW Guidelines 3  Deadline for CW is Friday 4:30pm 13th January 2017  Data will be posted on Moodle in ".mat" format (use load iris in Matlab)  Use Moodle assignment submission system to send only your Matlab .m file before the deadline  Files received after deadline will not be marked (must follow School procedures for late submission)  Write your own code (do not use any built-in functions to compute results, but you may use "pinv")  Entitle your file "surname_initial.m"CW Guidelines: Hints 4  Use provided examples to help construct your code  Use default margin vector bt = [1 1 1 ... 1 1 1] this should be the same length as the data vector (i.e. 100)  Need to pre-normalise data for Pseudoinverse method as described in tutorial  Plenty of help in Matlab and online – but do not cheat – I will be able to tell if you copy from each other! w  (YtY)1Ytb  Y†bCW Guidelines: Requirements 5  Must be able read "iris.mat" file from the root directory  Plot original data in Figure 1 (from iris.mat)  Plot classified test data in Figure 2 (same scale as Figure 1)  Use different colours and data points to differentiate classes  Should calculate and draw a weight vector line  Top marks for:-  Fully commented code  Clear flow of an efficient algorithm  Classification rate (Confusion Matrix)  Worth 20% of module!