| 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.學習人工智慧理論與優化方法 (認知)
2.培養實作高等人工智慧演算法實作與相關分析資料能力與優化方法（技能）
|
|
|
|
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
課程大綱, 人工智慧簡介 |
| Week 2 |
機器學習,深度學習與強化學習簡介 |
| Week 3 |
Machine Learning Data Processing 理論與實務 |
| Week 4 |
Linear regression |
| Week 5 |
Logistic regression |
| Week 6 |
SVM |
| Week 7 |
資訊安全概論 |
| Week 8 |
深度學習基礎 |
| Week 9 |
深度學習演算法理論與優化 |
| Week 10 |
Pytorch Programming |
| Week 11 |
Final Project Proposal |
| Week 12 |
資訊安全機器學習 I |
| Week 13 |
資訊安全機器學習 II |
| Week 14 |
資訊安全機器學習 III |
| Week 15 |
email 辨認 |
| Week 16 |
攻擊類別辨認
Final Project 報告 I
Final Project 報告 II |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
(1) Homework作業與程式設計 70%
(2) Final Project 30%
|
| Textbook & other References |
Textbook:
(1) 自行開發教材 ppt
(2) 網路公開課程與教材
|
| Teaching Aids & Teacher's Website |
http://awinlab.cs.nchu.edu.tw
|
| Office Hours |
| Monday, Wednesday 2:00-3:00 pm |
| Sustainable Development Goals, SDGs(Link URL) |
| 08.Decent Work and Economic Growth | include experience courses：N |
|