課程前半段先建立食品品質管制基本研究方法，後半段透過人工智慧結合IOT相關技術，可讓生產線上不良品的檢測流程進一步優化和調整，也可以應用在食品品質管制的領域中，其相關研發人員必須具備的"智慧影像辨識"技術，將在本課程當中做一深入淺出的學理介紹，並讓學生可以建立訓練模型，以及實際操作相關儀器設備以了解實務運用In the first half of the course, basic research methods for food quality control are established. In the second half, artificial intelligence is combined with IOT-related technologies to further optimize and adjust the detection process of defective products on the production line. It can also be applied to the field of food quality control and related research and development. The "smart image recognition" technology that personnel must possess will be introduced in this course in a simple and easy-to-understand manner, allowing students to build training models and actually operate relevant instruments and equipment to understand practical applications.
1. Introduction to statistical quality control, 8/e, by Douglas C. Montgomery.
2.楊慶忠，資料科學、智慧影像辨識與自然語言處理：Python+tf.Keras。
3.徐雅甄等，智慧商業管理，滄海書局，2022年。
1. Introduction to statistical quality control, 8/e, by Douglas C. Montgomery.
2. Yang Qingzhong, data science, intelligent image recognition and natural language processing: Python+tf.Keras.
3. Xu Yazhen et al., Smart Business Management, Canghai Bookstore, 2022.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 midterm exam |
30 | |
| 期末報告期末報告 Final report |
40 | |
| 課堂參與(作業與小考)課堂參與(作業與小考) Class participation (assignments and quizzes) |
30 |
