The goal of this lecture is to learn basic data mining methods using Weka (a data mining tool).
1. Machine Learning and Data Mining, Weka
2. Concept descriptions, applications
3. Concept space, bias
4. Input data format, classification, association
5. Clustering, numerical prediction
6. Types of attributes and their transformations
7. Knowledge representation, decision trees, classification rules
8. Association rules, instance-based representation, clusters
9. Basic learning algorithms, Naive Bayes
10. Decision trees, information gain, gain ratio
11. Covering algorithms, rules and decision trees
12. Evaluation of learning, cross validation
13. t-statistic, minimum description length
Data Mining: Practical Machine Learning Tools and Techniques (Third Edition)
I. H. Witten, E. Frank, Morgan Kaufmann, 2011.
Based on quizzes, assignments, and tests
Please visit the following site for more information about this lecture.