In this course, we will examine how data analysis technologies can be used to improve decision-making. We will study the fundamental principles and techniques of data mining, with real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, we will work "hands-on" with data mining software.
Topics covered will include:
Introduction to Data mining, Machine Learning and Artificial Intelligence: Concepts and Definitions, The Data Mining Process, Predictive and Descriptive tasks
Classification: Information and Attributes, Recursive partitioning and Decision Trees, Class-probability Estimation, Logistic Regression
Software: R, Matlab, and WEKA