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課程名稱 |
統計學習
Statistical Learning |
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開課學期 |
105-2 |
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授課對象 |
理學院 應用數學科學研究所 |
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授課教師 |
黃信誠 |
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課號 |
MATH5038 |
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課程識別碼 |
221 U6950 |
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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 |
天數305 |
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備註 |
要求必須修過機率及統計相關課程。
總人數上限：30人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1052MATH5038 |
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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 is a survey of statistical learning methods. It will cover major statistical learning methods and concepts. You will be responsible for a large amount of statistical programming in R. This will require a substantial amount of work outside the classroom, including going through “R labs” on your own. |
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課程目標 |
Students will learn how and when to apply statistical learning techniques, and understand their comparative strengths and weaknesses. |
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課程要求 |
Course Prerequisite: Introductory probability theory, introductory statistics, linear algebra, and some programming background in using R.
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
1. Hastie T., Tibshirani, R. and Friedman, J. (2009). The Elements of
Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd
edition (available online for free download at
http://statweb.stanford.edu/~tibs/ElemStatLearn/).
2. Hastie T., Tibshirani, R. and Wainwright, M. (2015). Statistical Learning
with Sparsity: The Lasso and Generalizations. Chapman and Hall/CRC
(available
online for free download at
http://web.stanford.edu/~hastie/StatLearnSparsity/).
3. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An
Introduction to Statistical Learning with Applications in R. Springer
(available online for free download at http://www-bcf.usc.edu/~gareth/ISL/).
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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. |
作業 |
40% |
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2. |
期中考 |
30% |
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3. |
期末報告 |
30% |
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週次 |
日期 |
單元主題 |
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第1週 |
02/23 |
Lecture 1-1 |
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第2週 |
03/02 |
Lecture 1-2 & Lecture 2-1 |
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第3週 |
03/09 |
Lecture 2-2 (Lasso) |
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第5週 |
03/23 & 03/30 |
Lecture 4-1 (Discriminant Analysis) |
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第6週 |
03/30 |
Lecture 4.2 (Logistic Regression) |
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第7週 |
04/06 |
Lecture 4.3 |
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第8週 |
04/13 |
Lecture 5 (SVM) |
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第9週 |
04/20 |
Lecture 6 (Unsupervised Learning) |
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第11週 |
05/11 |
Lecture 7 (Undirected Graphical Models) |
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第12週 |
05/18 |
Lecture 8 (Regression by Basis Functions) |
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第13週 |
05/25 |
Shiny |
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第14週 |
06/01 |
Lecture 9 (Trees Based Methods) |
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第15週 |
06/08 |
Lecture 10 (Artificial Neural Networks) |
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第4-5週 |
03/16 & 03/23 |
Lecture 3-1 & Lecture 3-2 |
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