課程大綱

課程資訊

課程名稱	統計生態學與程式語言應用 Computer Intensive Statistics in Ecology
開課學期	109-2
授課對象	學程永續資源學程
授課教師	謝志豪
課號	Ocean5052
課程識別碼	241EU1920
班次
學分	3.0
全/半年	半年
必/選修	選修
上課時間	星期四2,3,4(9:10~12:10)
上課地點
備註	本課程以英語授課。教室地點:計中110教室。與張以杰合授總人數上限：25人
Ceiba 課程網頁	http://ceiba.ntu.edu.tw/1092EcoStat
課程簡介影片
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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.
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.
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.
預期每週課後學習時數
Office Hours	另約時間
指定閱讀
參考書目	No textbook. Handouts and primary journal articles will be provided.
評量方式 (僅供參考)

課程進度

週次	日期	單元主題
Week 1	2/25	Introduction to R programming and plotting (Yi-Jay) 📷📷📷 First lecture online stream: https://youtu.be/Pbh4rKbH6CA
Week 2	3/04	Random variables, distribution, random number generator, statistical identity (Zac) 📷📷📷 lecture online stream: https://youtu.be/LXs5mVmQQMU
Week 3	3/11	Bootstrap and Jackknife (Zac) 📷📷📷 lecture online stream: https://youtu.be/X7vZbAebkmw
Week 4	3/18	Bootstrapped confidence limits (Zac) 📷📷📷 lecture online stream: https://youtu.be/paHEeMuOlfw
Week 5	3/25	Permutation (Zac) 📷📷📷 lecture online stream: https://youtu.be/dGuO3fogxdc
Week 6	4/01	Holiday
Week 7	4/08	Minimization (Zac) 📷📷📷 lecture online stream: https://www.youtube.com/watch?v=NWBbaht_nKk
Week 8	4/15	Classification 1 (Yi-Jay) 📷📷📷 lecture online stream: https://www.youtube.com/watch?v=m8r_larFBSE
Week 9	4/22	Classification 2 (Yi-Jay) 📷📷📷 lecture online stream: https://www.youtube.com/watch?v=3sWUX1F4w80
Week 10	4/29	海洋大會 (no class)
Week 11	5/06	Dimension reduction methods 1 (Hsiao-Hang) 📷📷📷 lecture online stream: https://www.youtube.com/watch?v=rdQ-BlCXw_s
Week 12	5/13	Dimension reduction methods 2 (Hsiao-Hang) 📷📷📷 lecture online stream: https://www.youtube.com/watch?v=dHV866Ehbwo
Week 13	5/20	Maximal likelihood (Yi-Jay) 📷📷📷 lecture online stream: https://youtu.be/Xt4cQ96Xsxo
Week 14	5/27	Model selection (Yi-Jay) 📷📷📷 lecture online stream: https://youtu.be/hplixkIqMs4
Week 15	6/03	Bayesian analysis I (Yi-Jay) 📷📷📷 lecture online stream: https://youtu.be/cxA5x5jcUHs
Week 16	6/10	Bayesian analysis II (Yi-Jay) 📷📷📷 lecture online stream: https://youtu.be/zpEHsvfppEc
Week 17	6/17	Neural Network (Zac) 📷📷📷 lecture online stream: https://meet.google.com/fmv-zwir-quz Video link: https://www.space.ntu.edu.tw/navigate/s/0937EFD86C2C41FFBAEF4BC86FCC3CC0QQY
Week 18	6/24	show final homework solution; no lecture