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 355287 Computer Algorithms 1 3.0 3 ★ RONG-SHUE HSIAO 4EECS2 23 0 選修。

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	355287	Computer Algorithms	1	3.0	3	★	RONG-SHUE HSIAO	4EECS2	23	0	選修。

Course Plan：

Instructor RONG-SHUE HSIAO

Email rshsiao@ntut.edu.tw

Last Updated 2026-02-25 00:10:13

Course Objective 介紹計算機演算法的設計與分析,內容包含了: 函數成長, 遞迴關係, 排序及序列統計, 基礎與進階資料結構, 動態規劃, 貪婪演算法, 搜尋, 圖形與幾何演算法, 字串比對, NP-completeness, 近似演算法, 及平行與分散式演算法. This course gives a broad introduction about the design and analysis of computer algorithms. General topics to be covered include: growth of functions, recurrences, sorting and order statistics, fundamental and advanced data structures, dynamic programming, greedy algorithms, searching, graph and geometry algorithms, string matching, NP-completeness, approximation algorithms, parallel and distributed algorithms.

Course Schedule Calendar: 1. No Class (2/28, 國定假日) 2. Mathematical Foundations: Introduction 3. Analyzing & Designing Algorithms 4. Growth of Functions, Recurrences 5. Heapsort 6. No Class (4/3, 國定假日) 7. Quicksort, Sorting in Linear Time 8. Mid-term Exam (in class) 9. No Class (4/24, 統測考場)，Mid-term Exam (take home) 10. No Class (5/1, 國定假日) 11. Medians, Order Statistics 12. Greedy Algorithms 13. Dynamic Programming (I) 14. Dynamic Programming (II) 15. Elementary Graph Algorithms & Minimum Spanning Trees 16. Shortest Paths Algorithms 17. No Class (6/19, 國定假日) 18. Final Exam

Evaluation and grading policy Grading: 30% Assignments & Term Project 30% Mid-term Exam (take home 40%, in class 60%) 40% Final Exam (take home 40%, in class 60%)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Second Ed., The MIT Press, 2001. (開發) 2. Michael T. Goodrich, Roberto Tamassia, and David M. Mount, Data Structures and Algorithms in C++, Second Edition, John Wiley & Sons, Inc, 2011. (ISBN-13 978-0-470-38327-8)

The access to curricular consultation rshsiao@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.
● 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.
● 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）
SDG5：性別平等（Gender Equality）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG12：負責任的消費與生產（Responsible Consumption and Production）
SDG17：夥伴關係（Partnerships for the Goals）

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）

Note
　

Instructor	RONG-SHUE HSIAO
Email	rshsiao@ntut.edu.tw
Last Updated	2026-02-25 00:10:13
Course Objective	介紹計算機演算法的設計與分析,內容包含了: 函數成長, 遞迴關係, 排序及序列統計, 基礎與進階資料結構, 動態規劃, 貪婪演算法, 搜尋, 圖形與幾何演算法, 字串比對, NP-completeness, 近似演算法, 及平行與分散式演算法. This course gives a broad introduction about the design and analysis of computer algorithms. General topics to be covered include: growth of functions, recurrences, sorting and order statistics, fundamental and advanced data structures, dynamic programming, greedy algorithms, searching, graph and geometry algorithms, string matching, NP-completeness, approximation algorithms, parallel and distributed algorithms.
Course Schedule	Calendar: 1. No Class (2/28, 國定假日) 2. Mathematical Foundations: Introduction 3. Analyzing & Designing Algorithms 4. Growth of Functions, Recurrences 5. Heapsort 6. No Class (4/3, 國定假日) 7. Quicksort, Sorting in Linear Time 8. Mid-term Exam (in class) 9. No Class (4/24, 統測考場)，Mid-term Exam (take home) 10. No Class (5/1, 國定假日) 11. Medians, Order Statistics 12. Greedy Algorithms 13. Dynamic Programming (I) 14. Dynamic Programming (II) 15. Elementary Graph Algorithms & Minimum Spanning Trees 16. Shortest Paths Algorithms 17. No Class (6/19, 國定假日) 18. Final Exam
Evaluation and grading policy	Grading: 30% Assignments & Term Project 30% Mid-term Exam (take home 40%, in class 60%) 40% Final Exam (take home 40%, in class 60%)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Second Ed., The MIT Press, 2001. (開發) 2. Michael T. Goodrich, Roberto Tamassia, and David M. Mount, Data Structures and Algorithms in C++, Second Edition, John Wiley & Sons, Inc, 2011. (ISBN-13 978-0-470-38327-8)
The access to curricular consultation	rshsiao@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. ● 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. ● 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） SDG5：性別平等（Gender Equality） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG12：負責任的消費與生產（Responsible Consumption and Production） SDG17：夥伴關係（Partnerships for the Goals）
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）
Note