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Part I Single Feature 1 . Create a matlab script that will perform each of the steps required for this exercise. 2 . Load partOneData

Part I Single Feature
1. Create a matlab script that will perform each of the steps required for this exercise.
2. Load partOneData.mat into the matlab environment (included in blackboard as part of the
assignment).
3. Create a histogram for each of the class distributions {classOne, classTwo}. Plot each of the
histograms on the same figure (use 100 bins). The figure should contain a title, legend and the x
and y axis should be labeled appropriately.
4. Report the prior probability of classOne? (Hint: Number of classOne samples divided by all
samples)
5. Report the prior probability of classTwo? (Hint: see above hint, but for classTwo)
6. Create a random partition of the data, splitting each of the classes into 60% training and 40%
testing.
a. Using only the training data, find the maximum likelihood estimator for the following
parameters:
i. Class One: ,\sigma Part I Single Feature
Create a matlab script that will perform each of the steps required for this exercise.
Load 'partOneData.mat' into the matlab environment (included in blackboard as part of the
assignment).
Create a histogram for each of the class distributions {classOne, classTwo}. Plot each of the
histograms on the same figure (use 100 bins). The figure should contain a title, legend and the x
and y axis should be labeled appropriately.
Report the prior probability of classOne? (Hint: Number of classOne samples divided by all
samples)
Report the prior probability of classTwo? (Hint: see above hint, but for classTwo)
Create a random partition of the data, splitting each of the classes into 60% training and 40%
testing.
a. Using only the training data, find the maximum likelihood estimator for the following
parameters:
i. Class One: ,
ii. Class Two: ,
b. Classify each of the samples in the test partition using a Bayesian classifier (you must
create a function that will do this). Report the prediction accuracy for class one and class
two.
Hint: You will need to create a method that, given the mean and standard deviation of a
distribution, determines the probability that 'x' belongs to the distribution.
Matlab template below:
function probability = computeGaussianDensity(mean, stdDev, x
(Your code to calculate the Gaussian density here)
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