Introduction to Data Mining

Description

Understand basics of probability Be able to apply Bayes’ theorem Understanding and be able to calculate simple aggregate statistics Understand the basics of supervised learning Understand instance based learning, tree learning, and rule induction Understand why uncertainty is important in learning and understand naïve Bayes Understand the importance of more advanced concepts such as ensemble methods and active learning and where and why they are applicable Understand the data mining process Understand association rule mining Understand clustering Ability to use a tool such as weka to analyze data and interpret results