本課程將以機器學習為基礎，探討電腦視覺，包含各項議題，例如工業檢測等案例
課程包含實作專題，讓學生在不同專案中學習業界專題經驗。
專題內容包含：輪廓比對Pattern Matching 、缺陷檢測Defect Detection、物件追蹤Object Tracking、區塊分析Blob Analysis、精密量測Measuring、自動對位Alignment、表面檢查Surface Inspection...等影像處理與影像分析相關技術以及系統整合實務，由授課老師以分組方式帶領修課學生實作專案。課程將邀請業界專家透過演講方式分享業界解題實務，並安排企業參訪，以瞭解第一線現場的實作環境。
教師群: 蔡清欉、陳隆彬、許瑞愷、陳倫奇、陳仕偉老師，每位老師在機器學習、電腦視覺都具備豐富經驗與專業
方式: 以分組進行，實作專題，主題為機器學習基礎與電腦視覺產業實務；教師與學生1-1指導This course will explore computer vision based on machine learning, including various topics, such as industrial inspection and other cases.
The course includes practical topics, allowing students to learn industry topic experience in different projects.
Thematic content includes: Pattern Matching, Defect Detection, Object Tracking, Blob Analysis, Precision Measuring, Automatic Alignment, Surface Inspection... and other image processing and image analysis related For technology and system integration practice, teachers lead students to implement projects in groups. The course will invite industry experts to share industry problem-solving practices through lectures, and arrange company visits to understand the implementation environment on the front line.
Teachers: Cai Qingtian, Chen Longbin, Xu Ruikai, Chen Lunqi, Chen Shiwei. Each teacher has rich experience and expertise in machine learning and computer vision.
Method: conducted in groups, implementing special topics, with the theme of machine learning basics and computer vision industry practice; teachers and students provide 1-1 guidance
1.自編教材
2.MIT Deep Learning Book https://github.com/janishar/mit-deep-learning-book-pdf
1. Self-compiled teaching materials
2.MIT Deep Learning Book https://github.com/janishar/mit-deep-learning-book-pdf
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中報告期中報告 interim report |
30 | |
| 期末專題發表期末專題發表 Final topic publication |
50 | |
| 平時表現平時表現 daily performance |
20 |
