Bayesian learning for classifying netnews text articles:

Naive Bayes classifiers are among the most successful known algorithms for learning to classify text documents. We will provide a dataset containing 20,000 newsgroup messages drawn from the 20 newsgroups. The dataset contains 1000 documents from each of the 20 newsgroups. 

1. For classes descriptions, please refer Table 6.3 of Dr. Mitchell's book (Machine Learning, Tom Mitchell, you can also download this table from  http://ranger.uta.edu/~heng/CSE6363_slides/Table6.3.pdf). 

2. Please download the data from http://www.cs.cmu.edu/afs/cs/project/theo-11/www/naive-bayes.html (Newsgroup Data)

3. Please use half data as training data, and the other half as testing data.

4. Please implement the Naive Bayes classifier by yourself. Don't use any online code.

5. Hand in your code and data as a whole package. Please make sure TA can easily run your code.

6. Hand in a simple report to summarize your implementations and results.

7. Send everything together via Blackboard. Instructions: 1) click our course link on blackboard. 2) click course materials and you will see assignment 1.