Statistical Aspects of Data Mining

Course Description: Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, neural networks, association rules, clustering, case based methods, and data visualization. The following chapters from the textbook will be covered in this order:
  • Chapter 1 - Introduction
  • Chapter 2 - Data
  • Chapter 3 - Exploring Data
  • Chapter 6 - Association Analysis: Basic Concepts and Algorithms
  • Chapter 4 - Classification: Basic Concepts, Decision Trees, and Model Evaluation
  • Chapter 5 - Classification: Alternative Techniques (naive bayes models, support vector machines)
  • Chapter 8 - Cluster Analysis: Basic Concepts and Algorithms
  • Chapter 10 - Anomaly Detection
Course Schedule and Lecture Notes in PDF format from Stanford for Statistical Aspects of Data Mining.