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 be based on machine learning and explore computer vision, including various issues, such as industrial testing and other cases
The course contains practical topics, allowing students to learn professional topic experience in different projects.
The topics include: contour comparison, Defect Detection, object tracking, Blob Analysis, Precision Measuring, Automatic Alignment, Surface Inspection, Surface Inspection, etc., and system integration practices. The instructors will lead the course students to make projects in a group manner. The course will invite industry experts to share industry-related explanations through lectures, and arrange business visits to understand the work environment on the first line.
Teacher Group: Teachers Cai Qing-shang, Chen Longbin, Xu Rui-chung, Chen Lunqi, and Chen Shiwei. Each teacher has rich experience and expertise in machine learning and computer vision.
Method: Perform in group, implement topics, the topic is based on machine learning and computer vision industry; 1-1 guidance from teachers and students
1.自編教材
2.MIT Deep Learning Book https://github.com/janishar/mit-deep-learning-book-pdf
1. Self-edited textbooks
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 Midterm Report |
30 | |
| 期末專題發表期末專題發表 Final issue release |
50 | |
| 平時表現平時表現 Normal performance |
20 |
