113年第1學期-1011 迴歸分析技術及應用 課程資訊
評分方式
評分項目 | 配分比例 | 說明 |
---|---|---|
Mid-term Examination | 40 | |
Final Examination | 40 | |
Assignments | 20 |
選課分析
本課程名額為 70人,已有57 人選讀,尚餘名額13人。
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授課教師
江輔政教育目標
1. This course highlights the importance and role of regression analysis (RA), a very useful approach for supervised learning. In particular, the regression modeling is a useful tool for predicting a quantitative response. Regression analysis has been around for a long time and is the topic of innumerable textbooks.
2. Though it may seem somewhat dull compared to some of the more modern statistical learning approaches, linear regression is still a useful and widely used machine learning method. This course will concentrate more on the applications of the regression modeling methodology with necessary mathematical details.
3. Some technical materials or articles regarding regression analysis (RA) will be provided for students to study, and the corresponding term reports are requested to write for scoring their evaluation as well. These training provide the students with valuable hands-on experience.
課程資訊
基本資料
必選課,學分數:3-0
上課時間:四/7,8,9[C107]
修課班級:資工系3B
修課年級:4年級以上
選課備註:AI組分組選修
教師與教學助理
授課教師:江輔政
大班TA或教學助理:尚無資料
Office Hour1. 星期一: 10:20~12:20 地點: ST 328 (科技大樓)
2. 星期四: 10:20~12:20 地點: ST 328 (科技大樓)
授課大綱
授課大綱:開啟授課大綱(授課計畫表)
(開在新視窗)
參考書目
Textbook(教科書):E-Book (本校圖書館有此教科書之電子資源)
Joe Suzuki, “Statistical Learning with Math and Python: 100 Exercises for Building Logic” (261 Pages), 2022. ISBN 978-981-15-7877-9 (eBook) https://doi.org/10.1007/978-981-15-7877-9
Reference Materials
1. 黃文隆,黃龍合編
“迴归分析”, 滄海書局出版(Tel 04-2708-8787)
ISBN 986-7287-08-8 (2014 三版)
2. G. James, “An Introduction to Statistical Learning with Applications in R”, ISBN 978-1-4614-7137-0, ISBN 978-1-4614-7138-7 (eBook) 441 pages (2013) (E-Book 本校圖書館有此教科書之電子資源)
開課紀錄
您可查詢過去本課程開課紀錄。 迴歸分析技術及應用歷史開課紀錄查詢