| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
讓學生了解數值分析所涵蓋的基本內容，做為日後計算科學或數據科學應用的工具或研究的基礎。
To enable students to understand the basic contents of numerical analysis as a tool or basis for research in future computational science or data science applications. |
| 1.Basic Knowledge in Mathematical Sciences |
| 2.Professional Knowledge in Mathematical Analysis |
| 7.Mathematical and Statistical software skills |
|
|
|
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Matlab/Python, Solve an equation for one variable. |
| Week 2 |
Solve an equation for one variable. |
| Week 3 |
Interpolation and Approximation Theory of Polynomials: Lagrange, Hermite, cubic spline |
| Week 4 |
Interpolation and Approximation Theory of Polynomials: Lagrange, Hermite, cubic spline |
| Week 5 |
Numerical differentiation and integration |
| Week 6 |
Numerical differentiation and integration |
| Week 7 |
Matrix computation |
| Week 8 |
Matrix computation & Midterm Exam |
| Week 9 |
Matrix computation |
| Week 10 |
Solve linear system of equations. |
| Week 11 |
Solve linear system of equations. |
| Week 12 |
Solving differential equations |
| Week 13 |
Solving differential equations |
| Week 14 |
Matrix and Data Analysis |
| Week 15 |
Matrix and Data Analysis |
| Week 16 |
Matrix and Data Analysis & Final Exam
|
self-directed
learning |
01.Participation in professional forums, lectures, and corporate sharing sessions related to industry-government-academia-research exchange activities.
03.Preparing presentations or reports related to industry and academia.
06.Participation in field trips and outdoor instructional activities at other NCHU campuses or branches, including experimental forests or test sites.
|
|
| Evaluation |
期中考（30%),期末考(30%),作業(40%)
Midterm (30%), Final exam (30%), Homework (40%) |
| Textbook & other References |
1.R.L. Bunder and J.D.Faires, Numerical Analsis, 9nd ed., Brooks/Cole, Cengage Learning, Boston, 2011. ISBN-13: 978-0-538-73351-9.
2.Jaan Kiusalaas, Numerical Methods in Engineering with Python, Cambridge University Press, New York, 2005. ISBN-13: 978-0-511-12810-3.
3. Cleve Moler, Numerical Computing with MATLAB, 2004, SIAM. (電子檔可以下載 https://www.mathworks.com/moler/chapters.html)
|
| Teaching Aids & Teacher's Website |
| Python 教學錄影網頁 https://www.pitt.edu/~naraehan/python3/getting_started_mac_first_try.html |
| Office Hours |
| 15 |
| Sustainable Development Goals, SDGs(Link URL) |
| 07.Affordable and Clean Energy 09.Industry, Innovation and Infrastructure | include experience courses：N |
|