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.
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 innocent 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 provides the students with valuable hands-on experience.
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)
Textbook (Textbook): E-Book (This school’s library has electronic resources for this textbook)
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. Huang Wenlong, edited by Huang Long
"Return Analysis", published by the Huahai Book Bureau (Tel 04-2708-8787)
ISBN 986-7287-08-8 (2014 Third Edition)
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 This school’s library has electronic resources for this textbook)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Mid-term ExaminationMid-term Examination Mid-term Examination |
40 | |
| Final ExaminationFinal Examination Final Examination |
40 | |
| AssignmentsAssignments Assignments |
20 |
