Download 統計資料採礦

Department of Statistics
Graduate Program
R253700
Statistical Data Mining
(統計資料採礦)
Spring 2013 (101 學年度第 2 學期)
1. This mission of the College is to serve business and society in the global economy through
developing quality and socially responsible professionals and business leaders.
2. The strategic objective of Department of Statistics is to cultivate quality professionals with
enthusiasm and global perspectives.
Graduate Program Learning Goals (goals covered by this course are indicated):

1
Graduate students should be able to communicate effectively verbally and in writing.

2
4
Graduate students should solve strategic problems with a creative and innovative approach.
Graduate students should demonstrate leadership skills and ethic demanded of a person in
authority.
Graduate students should possess a global economic and management perspective.
5
Graduate students should possess the necessary skills and values demanded of a true professional.
3
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 Instructor/開課教師:
Shuen-Lin Jeng /鄭順林
[email protected]
(06)2757575#53640
 Prerequisite/先修科目:
Statistics/統計學, Regression Analysis/迴歸分析
 Course Description/課程描述:
Statistical data mining is a popular research area lately. The main goal is to investigate the
high dimensional, large amount or complex data, and use statistical methods to provide
useful under covered information which can be critical for decision makers.
統計資料採礦是一門新近的熱門研究領域，主要探討如何在高維度、大量或複雜資料
中，使用統計方法發掘潛藏的有用資訊，以提供決策人員參考。
 Course Objectives/課程目標:
This course introduces the methods in data mining through the statistical point of view.
Using the software R and SQL Server, students will learn the ability to analyze massive and
complicated data and will be able to turn the raw data into valuable information.
本課程以統計角度 ，介紹資料採礦方法 ，並配合統計軟體 R和SQL Server的使用，
以培養學生從大量和複雜的資料中發掘資訊的能力。
 Course Content/課程內容:
1. Linear Methods for Regression;
2. Linear Method for Classification;
3. Basis Expansion;
4. Kernel Smoothing Methods;
5. Bayes classifier;
6. Model Assessment and Selection;
7. EM, MCMC and Bagging
8. Additive Models, Trees and MARS;
9. Boosting Trees
10. Neural Networks;
11. Support Vector Machines;
12. Prototype Methods and Nearest-Neighbors;
13. Association Rules;
14. Genetic algorithm;
15. Cluster Analysis;
16. Principle Components;
17. Independent Component Analysis;
18. Random Forests
19. High Dimensional Problems.
 Textbook/教科書:
The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2ed. By
Hastie, Tibshirani, Friedman, Springer, 2009 (全華).
 References/參考書目:
1. Introduction to Data Mining, 2ed, by Tan, Addison Wesley, 2013.
2. Data mining: concepts and techniques 3ed, by Han, Kamber and Pei. Elsevier 2012.
3. Introduction to Machine Learning 2ed, by Alpaydin, The MIT Press, 2010.
4. Data Mining Techniques: for marketing, sales, and customer relationship
management, 3ed, By Linoff and Berry. Wiley, 2011.
5. Data mining for Bioinfomatics, by Dua and Chowriappa. CRC press, 2013.
 Grading Policy/評量方式:
1. Homework 30% / 作業 30％
2. Project Report 30% / 計畫報告 30%
3. Final Exam 40% / 期末考 40％
Grading Policy for AACSB Multiple Assessment:
HW
30%
COMMU  Oral Commu./ Presentation
Project
30%
30%
Final
40%
CPSI
LEAD
 Written Communication
 Creativity and Innovation
 Problem Solving
 Analytical &
Computational Skills
 Leadership & Ethic
 Social responsibility
GLOB
 Global Awareness
VSP
 Values, Skills & Profess.
 Information Technology
 Management Skills
50%
30%
30%
20%
40%
20%
30%
50%