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 363454 Adaptive Signal Processing 1 3.0 3 ★ Ying-Ren Chien ECE 4 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	363454	Adaptive Signal Processing	1	3.0	3	★	Ying-Ren Chien	ECE	4	0

Course Plan：

Instructor Ying-Ren Chien　Office Hours

Email yrchien@ntut.edu.tw

Last Updated 2026-06-04 15:28:36

Course Objective Course contents include the following topics: 1. Design of fundamental linear adaptive filtering algorithms 2. Performance analysis of adaptive filtering systems 3. Design of nonlinear adaptive filtering algorithms 4. Distributed adaptive filtering systems 5. Study recent research papers related to adaptive filtering algorithms

Course Schedule (a) Week(b) Topics 1: Course Introduction 2–5: Design and Performance Analysis of LMS Adaptive Filtering Algorithms 6–8: Design of Kernel Adaptive Filtering Algorithms 9: Midterm Examination 10–11: Distributed Adaptive Filtering Systems 12–14: Paper Reading and Discussion 15–16: Final Project Oral Presentations Flexible Learning Weeks (Schedule and Assessment): 17: Independent Study / Final Written Report 18: Independent Study / Final Written Report

Evaluation and grading policy This course adopts a flipped classroom approach. Students are required to complete assigned readings before class and participate in in-class presentations and discussions. Grading policy: Midterm Examination: 25% Assigned Readings: 5% × 6 = 30% Final Project Oral Presentation: 15% Final Project Written Report: 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Adaptive Filtering Algorithms and Practical Implementation, Fifth Edition, by Paulo S. R. Diniz 2. Online Learning and Adaptive Filters Cambridge University Press, 2022. by Diniz, Paulo S. R.;de Campos, Marcello L. R.;Martins, Wallace A.;Lima, Markus V. S.;Apolinário, Jr, Jose A.

The access to curricular consultation Please make an appointment for discussion via e-mail.

Expanding teaching and resources ● 混成學習 (Blended Learning)
課程結合實體授課與線上學習（同步視訊／非同步活動）。
Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities).
● 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.

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）

Note
　

Instructor	Ying-Ren Chien　Office Hours
Email	yrchien@ntut.edu.tw
Last Updated	2026-06-04 15:28:36
Course Objective	Course contents include the following topics: 1. Design of fundamental linear adaptive filtering algorithms 2. Performance analysis of adaptive filtering systems 3. Design of nonlinear adaptive filtering algorithms 4. Distributed adaptive filtering systems 5. Study recent research papers related to adaptive filtering algorithms
Course Schedule	(a) Week(b) Topics 1: Course Introduction 2–5: Design and Performance Analysis of LMS Adaptive Filtering Algorithms 6–8: Design of Kernel Adaptive Filtering Algorithms 9: Midterm Examination 10–11: Distributed Adaptive Filtering Systems 12–14: Paper Reading and Discussion 15–16: Final Project Oral Presentations Flexible Learning Weeks (Schedule and Assessment): 17: Independent Study / Final Written Report 18: Independent Study / Final Written Report
Evaluation and grading policy	This course adopts a flipped classroom approach. Students are required to complete assigned readings before class and participate in in-class presentations and discussions. Grading policy: Midterm Examination: 25% Assigned Readings: 5% × 6 = 30% Final Project Oral Presentation: 15% Final Project Written Report: 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Adaptive Filtering Algorithms and Practical Implementation, Fifth Edition, by Paulo S. R. Diniz 2. Online Learning and Adaptive Filters Cambridge University Press, 2022. by Diniz, Paulo S. R.;de Campos, Marcello L. R.;Martins, Wallace A.;Lima, Markus V. S.;Apolinário, Jr, Jose A.
The access to curricular consultation	Please make an appointment for discussion via e-mail.
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 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.
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）
Note