- 担当教員
- 村田 剛志
- 使用教室
- 月5-6(W833)
- 単位数
- 講義：2 演習：0 実験：0
- 講義コード
- 76017
- シラバス更新日
- 2014年9月18日
- 講義資料更新日
- 2015年1月4日
- アクセス指標
-
- 学期
- 後期
講義の目的
The goal of this lecture is to learn basic data mining methods using Weka (a data mining tool).
講義計画
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
14. Summary
教科書・参考書等
Data Mining: Practical Machine Learning Tools and Techniques (Third Edition)
I. H. Witten, E. Frank, Morgan Kaufmann, 2011.
http://www.cs.waikato.ac.nz/~ml/weka/book.html
関連科目・履修の条件等
none
成績評価
Based on quizzes, assignments, and tests
その他
Please visit the following site for more information about this lecture.
http://www.ai.cs.titech.ac.jp/lecture/ml/
