HW06: Linear Discriminant Analysis 

HW06: Linear Discriminant Analysis 
In this homework, you will implement the linear discriminant analysis algorithm in R, Matlab, or Python. Here are the steps you need to follow: 1. You are given a data set, which contains 500 training and 500 test data points in the files hw06_mnist_training_digits.csv and hw06_mnist_test_digits.csv together with the labels in the files hw06_mnist_training_labels.csv and hw06_mnist_test_labels.csv.
2. Implement the linear discriminant analysis algorithm discussed in the lecture. (Hint: If the within-class scatter matrix is not invertible, you can, for example, add 1e-10 to its diagonal entries to get rid of the singularity.) 3. Calculate the two-dimensional projections of the training and test data points using the linear discriminant analysis by setting 𝑅 to 2. Draw the two-dimensional projections of the training and test data points. Your figures should be similar to the following figures.
4. Calculate the low-dimensional projections of the training and test data points using the linear discriminant analysis by changing 𝑅 from 1 to 9. For each low-dimensional representation, learn a 5-nearest neighbor classifier using the projections of training data points and calculate the classification accuracy on the projections of test data points. Draw the classification accuracy as a function of the dimensionality of the projected subspace. Your figure should be similar to the following figure.
What to submit: You need to submit your source code in a single file (.R file if you are using R, .m file if you are using Matlab, or .py file if you are using Python) and a short report explaining your approach (.doc, .docx, or .pdf file). You will put these two files in a single zip file named as STUDENTID.zip, where STUDENTID should be replaced with your 7-digit student number. How to submit: E-mail the zip file you created to mehmetgonen@ku.edu.tr with the subject line Intro2MachineLearningHW06. Please follow the exact style mentioned for the subject line and do not send a zip file named as STUDENTID.zip.