Introduction to Classification

Supervised learning tends to be easier to set-up than unsupervised learning, given the general clarity of the questions that are tackled and the ease with which we can evaluate a model’s performance. In some sense, classification and value estimation (regression) are the quintessential machine learning tasks; more so than unsupervised learning methods. In this course, we discuss the basics of classification, and some its issues and challenges. We also introduce decision trees, naïve Bayes classifiers, and discuss performance evaluation.