NCHU Course Outline
Course Name (中) 高等數值方法(一)(6597)
(Eng.) Advanced Numerical Methods (I)
Offering Dept Department of Applied Mathematics
Course Type Elective Credits 3 Teacher SHIH YIN TZER ect.
Department Department of Applied Mathematics/Graduate Language English Semester 2025-FALL
Course Description 矩陣計算是研讀AI、數據科學的基本工具︒本課程是研讀
矩陣計算的入門,除了數據模擬的基本概念外,本課
程能提供學生實用性的知識,當與實務數據結合使用
時,可以解決實際的問題︒
Matrix computation is a basic tool for studying AI and data science. This course is an introduction to studying matrix computation and analysis. In addition to the basic concepts of data simulation, this course can provide students with practical knowledge. After several home-works with practical data, students can solve practical problems. T



Prerequisites
self-directed learning in the course Y
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
1. Matrix theories, 2. SVD for imaging process, 3. Solve linear system and optimization, 4. Introduction to data science




Lecturing
Discussion
Exercises
Attendance
Written Presentation
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Matrix theories
Week 2 SVD for imaging process
Week 3 Solve linear system
Week 4 Orthogonality and the optimization
Week 5 Solve nonlinear function and optimization
Week 6 ODE Solvers
Week 7 ODE Solvers
Week 8 Numerical PDE solution: finite difference method
Week 9 Numerical PDE solution: finite difference method
Week 10 Numerical PDE solution: finite element method
Week 11 Numerical PDE solution: finite element method
Week 12 Numerical PDE solution: finite element method
Week 13 Tensor Decomposition
Week 14 Classification of Handwritten Digits
Week 15 Classification of Handwritten Digits
Week 16 PCA & MDS
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.

Evaluation
Homeworks and projects




Textbook & other References
1. Lars Elden, Matrix Methods in Data Mining and Pattern Recognition, SIAM 2007.
2. Golub & Von Loan, Matrix Computations, 3rd Ed. , John Hopkins University, 1996.
3. Yuan Yao, A Mathematical Introduction to Data Science, Bejing University, 2014
Teaching Aids & Teacher's Website

Office Hours
[office hours] Monday 1:00 - 3:00 PM



Sustainable Development Goals, SDGs(Link URL)
07.Affordable and Clean Energy   09.Industry, Innovation and Infrastructureinclude experience courses:N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day:2025/08/15 07:41:07 Printed Date, year/month/day:2025 / 8 / 19
The second-hand book website:http://www.myub.com.tw/