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

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.

基本資料

必選課，學分數：3-0
上課時間：四/7,8,9
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

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 [phone])
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)