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課程名稱 |
統計生態學與程式語言應用
Computer Intensive Statistics in Ecology |
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開課學期 |
106-2 |
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授課對象 |
學程 生物統計學程 |
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授課教師 |
謝志豪 |
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課號 |
Ocean5052 |
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課程識別碼 |
241EU1920 |
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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 |
星期四2,3,4(9:10~12:10) |
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上課地點 |
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備註 |
本課程以英語授課。教室地點:計中110教室。
與王慧瑜、張以杰合授
總人數上限：25人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1062Ocean5052_EcoSta |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
This is an advanced course intended for senior undergraduate and graduate students with knowledge of basic statistics including random variables, analysis of variance, regression analysis, and rank-based non-parametric statistics. We will discuss several computer-intensive statistical methods. We will discuss the theory, assumption, and application of these methods in ecological problems. |
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課程目標 |
The course is designed for hand-on work. Students need to get familiar with at least one computer language to do the statistics. Most of work can be done with R or MatLab, but any other programming language will do equally well. Sometimes, we will use well-developed software when the computation is too complicated and beyond the basic level. There will be dedicated time every week for students to present their works and to discuss the application of these methods on real world problems. |
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課程要求 |
Solve homework problems every week. There will be dedicated time every week for students to present their works and to discuss the application of these methods on real world problems. |
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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 |
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參考書目 |
No textbook. Handouts and primary journal articles will be provided. |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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Week 1 |
3/01 |
Introduction to R programming and plotting (Yi-Jay) |
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Week 2 |
3/08 |
Random variables, distribution, random number generator, statistical identity (Zac) |
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Week 3 |
3/15 |
Bootstrap (Hui-Yu) |
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Week 4 |
3/22 |
Jackknife (Hui-Yu) |
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Week 5 |
3/29 |
Bootstrapped confidence limits (Zac) |
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Week 6 |
4/05 |
Holiday |
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Week 7 |
4/12 |
Minimization (Zac) |
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Week 8 |
4/19 |
Classification 1 (Hui-Yu) |
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Week 9 |
4/26 |
Classification 2 (Hui-Yu) |
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Week 10 |
5/03 |
Dimension reduction methods 1 (Hui-Yu) |
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Week 11 |
5/10 |
Dimension reduction methods 2 (Hui-Yu) |
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Week 12 |
5/17 |
Maximal likelihood (Yi-Jay) |
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Week 13 |
5/24 |
Model selection (Yi-Jay) |
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Week 14 |
5/31 |
Bayesian analysis I (Yi-Jay) |
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Week 15 |
6/07 |
Bayesian analysis II (Yi-Jay) |
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Week 16 |
6/14 |
Permutation (Zac) |
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Week 17 |
6/21 |
Neural Network (Zac) |
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