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 363725 AI Deep Learning Application and Intelligent Control 1 3.0 3 ★ Shiao,Yaojung GVE 8 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	363725	AI Deep Learning Application and Intelligent Control	1	3.0	3	★	Shiao,Yaojung	GVE	8	0

Course Plan：

Instructor Shiao,Yaojung　Office Hours

Email yshiao@ntut.edu.tw

Last Updated 2026-06-17 13:20:57

Course Objective This course introduces the fundamental principles and basic applications of Artificial Intelligence (AI) Deep Learning (DL), and the theory and practical implementation of intelligent fuzzy control. The course combines theory and practice to make students understand AI DL, intelligent control, and their applications.

Course Schedule Week 1: Introduction to Artificial Intelligence and Fuzzy Control Week 2: Review of control system dynamics Week 3: Introduction to Deep Learning and Machine Learning Week 4: Convolutional Neural Networks (CNN) and CNN model Week 5: Image Preprocessing Week 6: Deep Learning Model Adjustment Week 7: Deep Learning and Image Recognition Week 8: project presentation Week 9: Fuzzy sets / Fuzzy relations / Fuzzy operation Week 10: workshop I/II - membership function Fuzzy control simulation Week 11: Fuzzy implications Week 12: Fuzzy logic control Week 14: Fuzzy controller design Week 15: Fuzzy controller design, writing exam Week 16: project presentation and demonstration wk17-18 auxiliary learning

Evaluation and grading policy Midterm report:30% Final project (reports, demonstration and presentation):35% Final writing exam:30% class performance: 5% * might be adjusted according to student learning situation

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
textbook: Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016, ISBN：9780262035613 reference book: 1 A Course in Fuzzy Systems and Control, by Li-Xin Wang, published by Prentice Hall 2. class notes

The access to curricular consultation yshiao@ntut.edu.tw

Expanding teaching and resources ● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
● 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 SDG4：優質教育（Quality Education）
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）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
In the weeks of auxiliary learning, self-learning for students is performed, and no scoring for this performance.

Instructor	Shiao,Yaojung　Office Hours
Email	yshiao@ntut.edu.tw
Last Updated	2026-06-17 13:20:57
Course Objective	This course introduces the fundamental principles and basic applications of Artificial Intelligence (AI) Deep Learning (DL), and the theory and practical implementation of intelligent fuzzy control. The course combines theory and practice to make students understand AI DL, intelligent control, and their applications.
Course Schedule	Week 1: Introduction to Artificial Intelligence and Fuzzy Control Week 2: Review of control system dynamics Week 3: Introduction to Deep Learning and Machine Learning Week 4: Convolutional Neural Networks (CNN) and CNN model Week 5: Image Preprocessing Week 6: Deep Learning Model Adjustment Week 7: Deep Learning and Image Recognition Week 8: project presentation Week 9: Fuzzy sets / Fuzzy relations / Fuzzy operation Week 10: workshop I/II - membership function Fuzzy control simulation Week 11: Fuzzy implications Week 12: Fuzzy logic control Week 14: Fuzzy controller design Week 15: Fuzzy controller design, writing exam Week 16: project presentation and demonstration wk17-18 auxiliary learning
Evaluation and grading policy	Midterm report:30% Final project (reports, demonstration and presentation):35% Final writing exam:30% class performance: 5% * might be adjusted according to student learning situation
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes textbook: Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016, ISBN：9780262035613 reference book: 1 A Course in Fuzzy Systems and Control, by Li-Xin Wang, published by Prentice Hall 2. class notes
The access to curricular consultation	yshiao@ntut.edu.tw
Expanding teaching and resources	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 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	SDG4：優質教育（Quality Education） 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） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	In the weeks of auxiliary learning, self-learning for students is performed, and no scoring for this performance.