This course requires basic knowledge of statistics and artificial intelligence.

Course Description

The course introduces students to fundamentals of data mining theory and algorithms. In addition to building a strong mathematical foundation, the course puts heavy emphasis on analysis and mining of actual data sets via popular data mining tools such as Weka, KNIME and R. The list of covered topics include classification (k-nearest neighborhood, classification tree, naïve Bayes, artificial neural networks), regression, clustering (k-means, fuzzy c-mean, hierarchical clustering) and association rules. Feature selection, data cleaning, data transformation, model evaluation and data visualization are also covered in sufficient details. By the end of this course, students are expected to have learned the art of modeling and interpreting large complicated data sets via predictive and descriptive data mining methods.

Course Outline

Overview of Data Mining

Definition, Original of Data Mining, Applications of Data Mining, Data Mining vs. OLAP and SQL

## Prerequisite

This course requires basic knowledge of statistics and artificial intelligence.## Course Description

The course introduces students to fundamentals of data mining theory and algorithms. In addition to building a strong mathematical foundation, the course puts heavy emphasis on analysis and mining of actual data sets via popular data mining tools such as Weka, KNIME and R. The list of covered topics include classification (k-nearest neighborhood, classification tree, naïve Bayes, artificial neural networks), regression, clustering (k-means, fuzzy c-mean, hierarchical clustering) and association rules. Feature selection, data cleaning, data transformation, model evaluation and data visualization are also covered in sufficient details. By the end of this course, students are expected to have learned the art of modeling and interpreting large complicated data sets via predictive and descriptive data mining methods.

## Course Outline

## Reference Books

## Marks Distribution