Implement a k-nearest neighbor algorithm discussed in The Elements of Statistical Learning and compare the performance of the algorithm on a dataset of your choice.

Write a machine learning program to classify variables using the logistic regression algorithm discussed in The Elements of Statistical Learning.

Create an artificial neural network using the back propagation method outlined in Pattern Recognition and Machine Learning and apply it to a supervised learning problem.

Using the support vector machines (SVM) as discussed in The Elements of Statistical Learning, develop a solution to a regression problem.

Compare the performance of linear regression models discussed in The Elements of Statistical Learning on a dataset of your choice.

Implement an expectation-maximization (EM) algorithm discussed in Pattern Recognition and Machine Learning and compare the performance on a dataset of your choice.

Derive a Bayes classifier using the techniques discussed in Pattern Recognition and Machine Learning and apply it to a classification problem.

Train a Hidden Markov Model (HMM) discussed in Pattern Recognition and Machine Learning and evaluate the results on a dataset of your choice.