The problem is to classify a document (that is, a given amount of text) as belonging to either computer science (CS) or non-CS. (This problem has applications in many areas, such as filtering email spam from non-spam.) For simplicity, our document for this project will consist of just a single sentence or part of a sentence (e.g., a phrase or a clause). Decide on a number of keywords in advance, and then represent a document as a vector of those keyword counts, where each component of the vector is integer-valued, representing the count (frequency) of the corresponding keyword in the document. Use a supervised learning scenario. You will create (choose) your own training and test data (creating the data is an important exercise). Create data by using randomly chosen sentences from, for example, your textbooks, works of fiction, or news. Use the Nave Bayes approach. Case I: The keyword counts in a document vector are each binary (1 or 0, representing presence or absence of that keyword in the document). Case II: The keyword counts are positive integers (possibly including zero).