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
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Lecturing |
Discussion |
Exercises |
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Attendance |
Written Presentation |
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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.
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Evaluation |
Homeworks and projects
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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 |
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Office Hours |
[office hours] Monday 1:00 - 3:00 PM
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Sustainable Development Goals, SDGs(Link URL) |
07.Affordable and Clean Energy   09.Industry, Innovation and Infrastructure | include experience courses:N |
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