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Introduction to Machine Learning (3) Data science, data analytics, and mining big data are terms used to describe the process of analyzing data sets in order to extract useful information from them. The tools necessary to carry out this kind of analysis come from a discipline known as machine learning. This course offers a broad introduction to the fascinating principles and paradigms underlying machine learning including the presentation of its main approaches and a discussion of its major theoretical issues. Students learn the inner workings of different kinds of classical supervised machine learning algorithms. Unsupervised paradigms are also covered, along with discussions of the new challenges faced by traditional machine learning methods. Students gain experience using machine learning tools and assessing their worth and limitations; are exposed to a practical case study in which they come up with solutions for dealing with a real world data analysis problem; and are initiated in the research process through the reading of research articles and the formulation and answering of research questions. Prerequisite: CSC-280, MATH-221, and STAT-202. Recommendation: basic programming skills, interest in solving simple math problems, and basic statistical knowledge.