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	364628	Neural Network	1	3.0	3	★	Cheng-Yen Chiang	MSAI	55	0

Course Plan：

Instructor	Cheng-Yen Chiang　Office Hours
Email	dcychiang@ntut.edu.tw
Last Updated	2026-08-21 16:56:35
Course Objective	典型神經網路之簡介與回顧、直傳式神經網路、監督式學習、非監督式學習、回傳式神經網路、神經網路之結構分析、學習方式分析神經網路之應用。 Introduction to traditional Networks, Feedback Neutral Networks ,Supervised Learning, Unsupervised Learning, Recurrant Neural Networks, Structure Analysis of Neural Networks, Analysis of Learning Methods, Applications of Neural Networks.
Course Schedule (Week 1-16)	Week 1_Syllabus and Overview Week 2_Introduction to Neural Network Week 3_Artificial Neural Network Architectures and Training Processes Week 4_Rosenblatt Perceptron Week 5_ADALINE Network and Delta Rule Week 6,7_Multilayer Perceptron Networks Week 8_Auxiliary Characteristics of MLP Week 9_Application of MLP Week 10_Radial Basis Function Networks Week 11_Recurrent Hopfield Networks Week 12,13_Self-Organizing Kohonen Networks Week 13,14_Learning Vector Quantization Week 15_Counter Propagation Networks Week 16_Adaptive Resonance Theory Networks Week 17,18_(Flexible Learning)Self learning on recent developments in Neural Networks
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) Content 觀看多部新穎神經網路、人工智慧或機器學習相關影片，理解影片介紹之技術、發展或應用內容，並撰寫學習報告，整理影片重點、心得與與課程內容之關聯。 Hours 6 Learning Outcomes 學生能理解課程以外之神經網路相關技術或應用，整理與分析影片內容，並能將影片所介紹之概念與課程所學之神經網路架構與學習方法進行連結與比較。 Assessment Percentage 20%：依據影片內容理解與整理、與課程內容之連結與分析、個人心得與反思、報告完整性等項目進行評量。
Evaluation and grading policy	作業與報告(Assignment and Report): 80% 期末作業(Final Assignment): 20%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes [1] Artificial Neural Networks - A Practical Course, by Da Silva et al. 2017 [2] Neural Networks and Learning Machines, by Simon Haykin. 2008 [3] Raschka, Sebastian, and Vahid Mirjalili. "Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2." Packt Publishing Ltd, 2019. [4] Nielsen, Michael A. "Neural networks and deep learning." Vol. 25. San Francisco, CA, USA: Determination press, 2015.
The access to curricular consultation	Email: dcychiang@ntut.edu.tw
Expanding teaching and resources	● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
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	● 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）
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