Course Syllabus

Course Information：

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

Spring Semester
Year 2026 359321 Special selected topics on control system engineering area 1 3.0 3 ★ Kuang-Yow Lian GEE 24 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359321	Special selected topics on control system engineering area	1	3.0	3	★	Kuang-Yow Lian	GEE	24	0

Course Plan：

Instructor Kuang-Yow Lian　Office Hours

Email kylian@ntut.edu.tw

Last Updated 2026-02-24 15:16:53

Course Objective 本課程著重於隨機信號估測的介紹、還有一些著名的機器學習方法的介紹。講述處理如音訊等信號時，如何描述信號、估測、分類、分析、決策，一系列處理過程中所需要用到的學理。

Course Schedule Main topics: - Review of Probabilities - Conditional Probabilities, Conditional Expectations - Minimum Mean Square Error (MMSE), Maximum-Likelihood Estimation, Bayesian Estimation, Kalman Filter - Data mining or Machine Learning (PCA, K-Means, Decision Tree, Clustering…)

Evaluation and grading policy HW 30 %, 期中指定報告30%, 期末專題報告40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Slides in English

The access to curricular consultation 1. 帶課助教將成立Line群組，藉以討論課程相關事宜；
2. 或於Office Hours至綜科館503討論課程。

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）

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
上課地點：綜科館 514 室
上課時間：每週三 9:10 ~ 12:00

Instructor	Kuang-Yow Lian　Office Hours
Email	kylian@ntut.edu.tw
Last Updated	2026-02-24 15:16:53
Course Objective	本課程著重於隨機信號估測的介紹、還有一些著名的機器學習方法的介紹。講述處理如音訊等信號時，如何描述信號、估測、分類、分析、決策，一系列處理過程中所需要用到的學理。
Course Schedule	Main topics: - Review of Probabilities - Conditional Probabilities, Conditional Expectations - Minimum Mean Square Error (MMSE), Maximum-Likelihood Estimation, Bayesian Estimation, Kalman Filter - Data mining or Machine Learning (PCA, K-Means, Decision Tree, Clustering…)
Evaluation and grading policy	HW 30 %, 期中指定報告30%, 期末專題報告40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Slides in English
The access to curricular consultation	1. 帶課助教將成立Line群組，藉以討論課程相關事宜； 2. 或於Office Hours至綜科館503討論課程。
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）
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	上課地點：綜科館 514 室上課時間：每週三 9:10 ~ 12:00