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The first half of the course is to establish basic research methods for food quality control. The second half of the course is to combine artificial intelligence with IOT-related technologies, which can further optimize and adjust the testing process for online defective products. It can also be applied in the field of food quality control, and its related research and development The "smart image identification" technology that personnel must have will provide an in-depth and detailed introduction to the science in this course, and students can establish training models and actually operate relevant instrument equipment to understand practical use.
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 identification and natural language processing: Python+tf.Keras.
3. Xu Yazhen et al., Smart Business Management, Huahai Books Bureau, 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 (work and exam) |
30 |
