| 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 Computational Science |
| 3.Professional Knowledge in Data Science |
| 4.Mathematical and Statistical Software Skills |
|
|
|
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction & Python入門
|
| Week 2 |
感知器 & Python實作
|
| Week 3 |
神經網路& Python實作
|
| Week 4 |
神經網路的學習& Python實作
|
| Week 5 |
誤差反向傳播法& Python實作 |
| Week 6 |
與學習有關的技巧& Python實作 |
| Week 7 |
卷積神經網路 & Python實作 |
| Week 8 |
期中考或報告 |
| Week 9 |
深度學習 & Python實作
|
| Week 10 |
人工智慧 |
| Week 11 |
KERAS簡介 |
| Week 12 |
利用KERAS深度學習人工智慧實務應用 |
| Week 13 |
TENSORFLOW 簡介 |
| Week 14 |
利用TENSORFLOW深度學習人工智慧實務應用 |
| Week 15 |
利用TENSORFLOW深度學習人工智慧實務應用 |
| Week 16 |
期末考或實習 |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
05.Participation in various workshops organized by different departments of NCHU.
|
|
| Evaluation |
小考/出席率（～60%）：補考成績*70%
學習筆記/作業（～20%）
大考/報告（～10%）
其他（～10%） |
| 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 |
|