本課程將提供在統計資料採礦會用到的工具和技巧，包含探索性數據分析、監督機器學習、非監督機器學習和模型評估。透過R程式語言培養學生靈活運用線性模型、決策樹、類神經網路、集群分析及關聯分析等資料採礦方法，讓本課程學生掌握數據分析及有效決策的能力。This course will provide tools and techniques used in the mining of statistical data, including exploratory data analysis, supervisory machine learning, non-surveillance machine learning, and model evaluation. Through R program language, students can use linear models, decision trees, neural networks, cluster analysis and related analysis to use mining methods, so that students in this course can master the ability of data analysis and effective decision-making.
統計資料採礦主要應用統計方法、資料分析和機器學習演算法，進行資料庫之知識挖掘，藉此從高維度大型資料庫中挖掘潛藏的有用資訊。目前已在金融、經濟、行銷、電子商務、數位資訊產業、高科技產業、生命科學和醫學等不同領域，逐漸提升地位成為決策輔助系統中的重要元件之一，提供管理階層決策輔助之用。
The main application of statistical data mining is to use statistical methods, data analysis and machine learning algorithms to conduct knowledge mining of databases, thereby mining hidden useful information from large databases of high-dimensionality. At present, it has gradually improved its position in different fields such as finance, economy, marketing, e-commerce, digital information industry, high-tech industry, life sciences and medicine, and has gradually become one of the important components in the decision-making assistance system, providing management-level decision-making assistance.
"An Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The book's website is http://faculty.marshall.usc.edu/gareth-james/ISL/
You are able to access the book online from the THU library http://webpac.lib.thu.edu.tw/bookDetail.do?id=959395
"An Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The book's website is http://faculty.marshall.usc.edu/gareth-james/ISL/
You are able to access the book online from the THU library http://webpac.lib.thu.edu.tw/bookDetail.do?id=959395
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 隨堂習作隨堂習作 In the Hall of Works |
40 | 每次上課的前20~30分鐘有線上或紙本習作，請勿遲到，以免自身權益受損 |
| 期中考期中考 Midterm exam |
20 | |
| 小組期末報告小組期末報告 Group final report |
40 |
