Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2026 363517 Applications of Artificial Neural Networks 1 3.0 3 ★ FANG-CHIH TIEN GIEM 4 0 工管所和工管四甲乙合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	363517	Applications of Artificial Neural Networks	1	3.0	3	★	FANG-CHIH TIEN	GIEM	4	0	工管所和工管四甲乙合開

Course Plan：

Instructor FANG-CHIH TIEN　Office Hours

Email fctien@ntut.edu.tw

Last Updated 2026-06-16 20:48:04

Course Objective 本課程之主要目的除介紹基本之類神經理論外，更在於運用現有之類神經網路於工業工程領域內之相關運用，將以現有之資料庫實地解決分類、分群、預測、監控等實際問題，使學生能夠了解類神經網路之基礎理論外，更可深入運用此工具。 The objective of this class is to explore the basic theoretical basis and applications of Neural Networks and their applications. In this course, students will learn how to build deep learning networks and their applications in machine vision areas 加強程式、邏輯訓練及實作 (PyTorch & python & MVA – prerequisites) The major topics of networks covered include: Block Design: Residual block, SE block, etc. AD(Anomaly Detection): EfficientAD、PatchCore、Padim Classifier: CNN (ResNet, DenseNet, EfficientNetv2) OD (Object Detection): Yolov10 BBox and Instance segmentation、RMDet Semantic Segmentation: AutoEncoder, UNet GAN: DCGAN etc. 另請同學自備個人電腦，上課時需同時使用

Course Schedule The content of this course include the following subjects： 1. PyTorch: (3 weeks) a. Basic commands & nn.module related commands b. Gradient descent & BPN c. CNN & LeNet d. Dataset & Dataloader e. Loss Function & Transformation 2. CNN Training Tips & CAM Heatmaps, Visualization (2 week) 3. Classifier: ResNet, DenseNet, EfficientNet (2 weeks) 4. OD (Object Detection): FRCNN, Yolo, Mask RCNN, Labeling (2 weeks) 5. AD (Anomaly Detection): EfficientAD, PaDim, PatchCore (2 weeks) 6. Semantic Segmentation: AE, Unet (2 Weeks) 7. GAN: DCGAN, etc. (2 Weeks)

Evaluation and grading policy Assignment & Quiz 35 % Final Test 25% Paper Study and Presentation 10 % Term Project - Competition 30 % AC Book Reading 5% (optional)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Satish Kumar, Neural Networks— a classroom approach, McGrawHill, 滄海書局 (Textbook) Simon, Haykin, Neural Network and Learning Machine, Pearson International Youtubes: 田方治: MVA、Deep Learning Others Personal power points

The access to curricular consultation Google meet 上課連結: https://meet.google.com/pwu-amgf-sgm

Expanding teaching and resources ● 數位科技融入 (Digital Technology Integration)
運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。
Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
1. 線上上課
2. 連結: https://meet.google.com/pwu-amgf-sgm
3. office Hours: 週三上午 09:00~11:00

Instructor	FANG-CHIH TIEN　Office Hours
Email	fctien@ntut.edu.tw
Last Updated	2026-06-16 20:48:04
Course Objective	本課程之主要目的除介紹基本之類神經理論外，更在於運用現有之類神經網路於工業工程領域內之相關運用，將以現有之資料庫實地解決分類、分群、預測、監控等實際問題，使學生能夠了解類神經網路之基礎理論外，更可深入運用此工具。 The objective of this class is to explore the basic theoretical basis and applications of Neural Networks and their applications. In this course, students will learn how to build deep learning networks and their applications in machine vision areas 加強程式、邏輯訓練及實作 (PyTorch & python & MVA – prerequisites) The major topics of networks covered include: Block Design: Residual block, SE block, etc. AD(Anomaly Detection): EfficientAD、PatchCore、Padim Classifier: CNN (ResNet, DenseNet, EfficientNetv2) OD (Object Detection): Yolov10 BBox and Instance segmentation、RMDet Semantic Segmentation: AutoEncoder, UNet GAN: DCGAN etc. 另請同學自備個人電腦，上課時需同時使用
Course Schedule	The content of this course include the following subjects： 1. PyTorch: (3 weeks) a. Basic commands & nn.module related commands b. Gradient descent & BPN c. CNN & LeNet d. Dataset & Dataloader e. Loss Function & Transformation 2. CNN Training Tips & CAM Heatmaps, Visualization (2 week) 3. Classifier: ResNet, DenseNet, EfficientNet (2 weeks) 4. OD (Object Detection): FRCNN, Yolo, Mask RCNN, Labeling (2 weeks) 5. AD (Anomaly Detection): EfficientAD, PaDim, PatchCore (2 weeks) 6. Semantic Segmentation: AE, Unet (2 Weeks) 7. GAN: DCGAN, etc. (2 Weeks)
Evaluation and grading policy	Assignment & Quiz 35 % Final Test 25% Paper Study and Presentation 10 % Term Project - Competition 30 % AC Book Reading 5% (optional)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Satish Kumar, Neural Networks— a classroom approach, McGrawHill, 滄海書局 (Textbook) Simon, Haykin, Neural Network and Learning Machine, Pearson International Youtubes: 田方治: MVA、Deep Learning Others Personal power points
The access to curricular consultation	Google meet 上課連結: https://meet.google.com/pwu-amgf-sgm
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	1. 線上上課 2. 連結: https://meet.google.com/pwu-amgf-sgm 3. office Hours: 週三上午 09:00~11:00