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 366432 Deep Learning and Internet of Things 1 3.0 3 ★ Yibeltal Chanie Manie GEOE
IEECSG 0 0 光電所和電資外生所合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366432	Deep Learning and Internet of Things	1	3.0	3	★	Yibeltal Chanie Manie	GEOE IEECSG	0	0	光電所和電資外生所合開

Course Plan：

Instructor Yibeltal Chanie Manie　Office Hours

Email yibeltal_chanie@mail.ntut.edu.tw

Last Updated 2026-06-05 14:46:29

Course Objective 1.深度學習介紹，2.監督式學習，3.無監督式學習，4.物聯網，5.感知層，6.網路層，7.應用層，8. 深度學習與物聯網應用實例。 1. Introduction to Deep Learning, 2. Supervised Learning, 3. Unsupervised Learning, 4. Internet of Things, 5. Perception Layer, 6. Network Layer, 7. Application Layer, 8. Deep Learning and Internet of Things

Course Schedule Week 1-2. Introduction to Deep Learning week 3-4 Fundamental of Internet of Things Week 5-6. Neural Network Architectures Week 7-8. Convolutional Neural Networks Week 9-10 . Recurrent Neural Networks (RNNs) Week 11-12. Image classification and Object detection Week 13. Generative Models Week 14 Transformers & Attention Week 15-16. Application of Deep learning for Internet of things Week 17-18. Final project presentation

Evaluation and grading policy Assignments : 40 % Labs / Mini Projects: 20 % Attendance : 10 % Final Project : 30 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
References : 1. Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. 2016, MIT. 2. B.K. Tripathy, J. Anuradham: Internet of Things(IoT): Technologies, Applications, Challenges and Solutions, CRC Press, Taylor and Francis, 2017 3. Anirudh Koul, Siddha Ganju, Meher Kasam: Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition

The access to curricular consultation email: yibeltal_chanie@mail.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 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）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 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
　

Instructor	Yibeltal Chanie Manie　Office Hours
Email	yibeltal_chanie@mail.ntut.edu.tw
Last Updated	2026-06-05 14:46:29
Course Objective	1.深度學習介紹，2.監督式學習，3.無監督式學習，4.物聯網，5.感知層，6.網路層，7.應用層，8. 深度學習與物聯網應用實例。 1. Introduction to Deep Learning, 2. Supervised Learning, 3. Unsupervised Learning, 4. Internet of Things, 5. Perception Layer, 6. Network Layer, 7. Application Layer, 8. Deep Learning and Internet of Things
Course Schedule	Week 1-2. Introduction to Deep Learning week 3-4 Fundamental of Internet of Things Week 5-6. Neural Network Architectures Week 7-8. Convolutional Neural Networks Week 9-10 . Recurrent Neural Networks (RNNs) Week 11-12. Image classification and Object detection Week 13. Generative Models Week 14 Transformers & Attention Week 15-16. Application of Deep learning for Internet of things Week 17-18. Final project presentation
Evaluation and grading policy	Assignments : 40 % Labs / Mini Projects: 20 % Attendance : 10 % Final Project : 30 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References : 1. Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. 2016, MIT. 2. B.K. Tripathy, J. Anuradham: Internet of Things(IoT): Technologies, Applications, Challenges and Solutions, CRC Press, Taylor and Francis, 2017 3. Anirudh Koul, Siddha Ganju, Meher Kasam: Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition
The access to curricular consultation	email: yibeltal_chanie@mail.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	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） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 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