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課程名稱 |
探索式多變量資料分析
Exploratory Multivariate Data Analysis |
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開課學期 |
107-1 |
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授課對象 |
共同教育中心 統計碩士學位學程 |
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授課教師 |
周呈霙 |
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課號 |
BME7909 |
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課程識別碼 |
631 M3110 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一7,8,9(14:20~17:20) |
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上課地點 |
知207 |
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備註 |
工程與環境統計領域選修課程之一。
總人數上限：30人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1071BME7909_EDA |
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課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
This course covers methods for analyzing multivariate data.
1. Graphical methods
2. Modeling and inference using the multivariate normal distribution
• Multivariate data and models
• Multivariate Normal distribution
• Traditional inference: Multivariate Regression, MANOVA, etc.
• Links with mixed linear models and hierarchical modeling.
3. Exploratory techniques based on eigenvalue and singular decomposition
• SVD of a data matrix; special decomposition
• Principle Component Analysis
• Factor Analysis
• Canonical Correlation
4. Classification and Clustering
• Linear Discrimination
• Classification Trees
• Hierarchical Clustering
• K-means Clustering
• Dimension Reduction
• Multidimensional Scaling
5. Functional data analysis
• Functional PCA
• Functional Classification
• Functional Clustering
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課程目標 |
To learn how to analyze data sets and summarize their main characteristics through visual methods or statistical models.
To explore what the data can tell us beyond the formal modeling or hypothesis testing tasks. |
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課程要求 |
待補 |
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
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指定閱讀 |
1.Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson " Multivariate Data Analysis" 7th Edition.
2. Husson, F., Le, S., and Pages, J. 2010 “Exploratory Multivariate Analysis by Example Using R”, CPC Press.
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參考書目 |
待補 |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
In class participation |
10% |
Via zuvio |
2. |
Homework |
10% |
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3. |
Project and presentation |
20% |
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4. |
Midterm exam |
30% |
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5. |
Final exam |
30% |
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週次 |
日期 |
單元主題 |
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第1週 |
9/10 |
Course overview and introduction
http://www.statedu.ntu.edu.tw/lecture/index.asp |
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第2週 |
9/17 |
Introduction of R (R overview slides updated) |
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第3週 |
9/24 |
Mid-Autumn Festival (No class) |
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第4週 |
10/01 |
Data visualization and hypothesis of means |
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第5週 |
10/08 |
Multiple Regression |
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第6週 |
10/15 |
Multiple regression and MANOVA |
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第7週 |
10/22 |
Feature selection |
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第8週 |
10/29 |
Eigenvalue decomposition, singular value decomposition, PCA |
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第9週 |
11/05 |
Midterm exam |
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第10週 |
11/12 |
Profile analysis |
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第11週 |
11/19 |
Factor Analysis |
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第12週 |
11/26 |
Factor analysis |
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第14週 |
12/10 |
Canonical correlation analysis |
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第15週 |
12/17 |
Clustering |
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第16週 |
12/22 |
Classification |
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第17週 |
12/24 |
分組報告，請各組將投影片及相關資料上傳至https://drive.google.com/drive/folders/1a0V_U8jGUbGDgIUBCPgzFhgkD062HKde?usp=sharing |
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