| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
Linear Algebra really will open up possibilities of working and manipulating data. There are many awesome applications of Linear Algebra in Data Science, and have broadly categorized the applications into four fields:
1. Machine learning
2. Dimensionality Reduction
3. Natural Language Processing (NLP)
4. Computer Vision
So you can deep dive further into the one(s) which grabs your future research. |
| 1.數學專業思維與邏輯推理知識 |
| 2.數學分析專業知識 |
| 3.計算科學專業知識 |
| 5.數學模型建構與軟體應用 |
| 6.計算與模擬研究 |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共18週) |
| 週次 |
授課內容 |
| 第1週 |
Fundamental background review in linear algebra
|
| 第2週 |
Fundamental background review in linear algebra
|
| 第3週 |
Orthogonality and the optimization |
| 第4週 |
Orthogonality and the optimization |
| 第5週 |
SVD and image processing |
| 第6週 |
SVD and image processing |
| 第7週 |
Tensor Decomposition |
| 第8週 |
Tensor Decomposition |
| 第9週 |
Review project #1 and presentation |
| 第10週 |
Clustering and NMF |
| 第11週 |
Clustering and NMF |
| 第12週 |
Classification of Handwritten Digits |
| 第13週 |
Classification of Handwritten Digits |
| 第14週 |
PCA & MDS |
| 第15週 |
PCA & MDS |
| 第16週 |
Review project #2 and presentation |
| 第17週 |
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| 第18週 |
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| 學習評量方式 |
Presentation(20%)
HWs and present(80%) |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
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 |
| 課程教材（教師個人網址請列在本校內之網址） |
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| 課程輔導時間 |
| Monday 2:00-4:00 PM with appointment |
| 聯合國全球永續發展目標 |
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