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. |
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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 |
Week 17 |
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Week 18 |
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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 ww.mathworks.com/moler.)
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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 |
| include experience courses:N |
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