| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 以經典機器學習演算法,基礎python程式設計,數據科學基礎數學為課程學習目標 |
| 2.Professional Knowledge in Computational Science |
| 4.Mathematical and Statistical Software Skills |
|
|
| Discussion |
| Practicum |
| Lecturing |
|
| Attendance |
| Oral Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
課程簡介 |
| Week 2 |
Sage軟體與Python實作環境介紹 |
| Week 3 |
基礎程式設計
|
| Week 4 |
基礎程式設計 |
| Week 5 |
數據科學基礎數學與Python實作一 |
| Week 6 |
數據科學基礎數學與Python實作二 |
| Week 7 |
機器學習演算法應用一:K-Nearest Neighbor(KNN) |
| Week 8 |
機器學習演算法應用二:K-means clustering(k-means) |
| Week 9 |
機器學習演算法應用三:Support vector machine(SVM) |
| Week 10 |
機器學習演算法應用四:Principal component analysis(PCA) |
| Week 11 |
機器學習演算法應用五:Linear and polynomial regressions |
| Week 12 |
機器學習演算法應用六:Logistic regression |
| Week 13 |
機器學習演算法應用七:Decision tree |
| Week 14 |
解方程組演算法應用一:Euclidean algorithm |
| Week 15 |
解方程組演算法應用二:Gaussian elimination |
| Week 16 |
解方程組演算法應用三:Groebner bases
最佳化演算法應用:lagrange multiplier
期末小組報告 |
self-directed
learning |
|
|
| Evaluation |
| 課堂參與(20%)+作業(60%)+期末報告(20%) |
| Textbook & other References |
| TBA |
| Teaching Aids & Teacher's Website |
| I-Learning |
| Office Hours |
| 69 |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|