| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 首要目標為培養研究生的基本類神經網路觀念、人工智慧理論和深度學習架構。尤其強調嚴謹的演算過程以及Python 程式之撰寫與實作。 |
| 2.Professional Knowledge in Scientific Computation |
| 3.Professional Knowledge in Data Science |
| 4.Mathematical and Statistics Software Skill |
|
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|
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| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
1. Introduction & Python入門
2. 感知器 & Python實作
3. 神經網路& Python實作
4. 神經網路的學習& Python實作
5. 誤差反向傳播法& Python實作
6. 與學習有關的技巧& Python實作
7. 卷積神經網路 & Python實作
8. 深度學習 & Python實作
9. 人工智慧
11. KERAS簡介
11. 利用KERAS深度學習人工智慧實務應用
12. TENSORFLOW 簡介
13. 利用TENSORFLOW深度學習人工智慧實務應用 |
| Week 2 |
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| Week 3 |
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| Week 4 |
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| Week 5 |
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| Week 6 |
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| Week 7 |
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| Week 8 |
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| Week 9 |
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| Week 10 |
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| Week 11 |
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| Week 12 |
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| Week 13 |
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| Week 14 |
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| Week 15 |
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| Week 16 |
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self-directed
learning |
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| Evaluation |
1.平時成績（平時考，出席率，課堂互動，作業等） ：佔60%
2.期中考(報告)成績：佔20%
3.期末考（報告）成績 ：佔20% |
| Textbook & other References |
「用Python進行深度學習的基礎理論實作:Deep Learning」，齊藤康毅著,吳嘉芳翻譯
「Neural Network Design」，Hagan; Demuth; Beale
「Neural Networks: A Classroom Approach 2/e」， , by Satish Kumar, McGraw-Hill Publishing, 2013. ISBN：9781259006166 東華
「Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow 2/e」，by Aurélien Géron, 2019 |
| Teaching Aids & Teacher's Website |
| https://www.youtube.com/channel/UCSivAooQ-OTLATS1dTT3DZw |
| Office Hours |
| 再另行公告 |
| Sustainable Development Goals, SDGs(Link URL) |
| | include experience courses：N |
|