1. (In Matlab) Create 5 random partitions of the data, splitting each of the classes into 60% training and 40% testing.
   1. Using only the training data, find the maximum likelihood estimator for the following parameters:
      1. Class One: ,
      2. Class Two: ,
   2. Classify each of the test samples using a Bayesian classifier (you must create a function that will do this).
2. Report the mean and standard deviation for the prediction accuracy from step 6.

Hint: You will need to create a method that, given the mean and standard deviation of a distribution, determines the probability of a value x belonging to that distribution.

Matlab template below:

function probability = computeGaussianDensity(mean, stdDev, x)

Import Wizard Select variables to import using checkboxes Create variables matching preview Create vectors from each column using column names Create vectors from each row using row names Variables in/Users/m.takahashi/Desktop/school/CSC1410-Pattern Recognition/Week 1/partOneData.mat Import Name Bytes Class ize 4 classOne 1x10000 clasST 1x10000 80000 double 80000 double 5.26436.1353 5.0989 5.3168 9.2997 6.7788 9.1