Course Syllabus

Course Information：

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

Fall Semester
Year 2025 348769 Introduction to Artificial Intelligence and Computer Programming 1 2.0 3 ▲ Lee, Ming-Tsung 4CEB1C 0 0 計中電腦教室

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	348769	Introduction to Artificial Intelligence and Computer Programming	1	2.0	3	▲	Lee, Ming-Tsung	4CEB1C	0	0	計中電腦教室

Course Plan：

Instructor Lee, Ming-Tsung　Office Hours

Email mtlee@ntut.edu.tw

Last Updated 2025-05-20 11:03:13

Course Objective This course focuses on understanding the algorithms behind programming solutions rather than the syntax of any specific programming language. Students will learn how to conceptualize, develop, and implement algorithms to solve engineering problems using a dual-language approach. Fortran will be used to introduce algorithmic fundamentals, while Python—generated and explored through ChatGPT—will demonstrate how logical structures can be adapted across languages. A key emphasis of this course is cultivating literacy in using Generative AI tools, especially ChatGPT, for programming. Students will learn how to prompt ChatGPT effectively, interpret and critique AI-generated code, and use it as a tool to accelerate their understanding of computational thinking. The course aims to empower students to confidently build adaptable, reliable solutions for engineering applications, especially those involving data analysis, simulation, and visualization.

Course Schedule Part I: Foundations and Tools (Weeks 1–3) Introduction to programming and ChatGPT as a GenAI assistant Basic Linux operations, shell usage, and the vim editor Setting up compilers and interpreters; visualization with gnuplot Role of spreadsheets (Excel) in conceptual planning Part II: Programming Concepts with Fortran (Weeks 4–10) Input/Output (I/O) fundamentals Variable declaration and data types Mathematical operations and built-in functions Control structures: loops (do), conditionals (if) File operations and array handling Subroutines and modules Introduction to random number generation * During these sessions, Python equivalents will be generated using ChatGPT to reinforce the portability of algorithmic thinking. Part III: Applied Algorithms and Visualization (Weeks 11–16) Sorting algorithms and data organization Statistical analysis and data interpretation Probability functions and histogram plotting Regression modeling and trend analysis Generating random walk trajectories and visualizing with VMD Simulating self-assembly and analyzing aggregate structures

Evaluation and grading policy Class Participation (Zuvio attendance and Q&A) 20% In-Class Exercises (hands-on programming tasks) 40% Final Project Report (individual or group-based) 40% There are no midterm or final exams. Attendance is mandatory, and students are expected to actively participate in in-class discussions and exercises.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Instructor-prepared materials will be used throughout the course.

The access to curricular consultation A LINE group will be created at the beginning of the semester for course communication.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
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）

Does the course incorporate AI AI1：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）
AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
AI3：使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）

Note
　

Instructor	Lee, Ming-Tsung　Office Hours
Email	mtlee@ntut.edu.tw
Last Updated	2025-05-20 11:03:13
Course Objective	This course focuses on understanding the algorithms behind programming solutions rather than the syntax of any specific programming language. Students will learn how to conceptualize, develop, and implement algorithms to solve engineering problems using a dual-language approach. Fortran will be used to introduce algorithmic fundamentals, while Python—generated and explored through ChatGPT—will demonstrate how logical structures can be adapted across languages. A key emphasis of this course is cultivating literacy in using Generative AI tools, especially ChatGPT, for programming. Students will learn how to prompt ChatGPT effectively, interpret and critique AI-generated code, and use it as a tool to accelerate their understanding of computational thinking. The course aims to empower students to confidently build adaptable, reliable solutions for engineering applications, especially those involving data analysis, simulation, and visualization.
Course Schedule	Part I: Foundations and Tools (Weeks 1–3) Introduction to programming and ChatGPT as a GenAI assistant Basic Linux operations, shell usage, and the vim editor Setting up compilers and interpreters; visualization with gnuplot Role of spreadsheets (Excel) in conceptual planning Part II: Programming Concepts with Fortran (Weeks 4–10) Input/Output (I/O) fundamentals Variable declaration and data types Mathematical operations and built-in functions Control structures: loops (do), conditionals (if) File operations and array handling Subroutines and modules Introduction to random number generation * During these sessions, Python equivalents will be generated using ChatGPT to reinforce the portability of algorithmic thinking. Part III: Applied Algorithms and Visualization (Weeks 11–16) Sorting algorithms and data organization Statistical analysis and data interpretation Probability functions and histogram plotting Regression modeling and trend analysis Generating random walk trajectories and visualizing with VMD Simulating self-assembly and analyzing aggregate structures
Evaluation and grading policy	Class Participation (Zuvio attendance and Q&A) 20% In-Class Exercises (hands-on programming tasks) 40% Final Project Report (individual or group-based) 40% There are no midterm or final exams. Attendance is mandatory, and students are expected to actively participate in in-class discussions and exercises.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Instructor-prepared materials will be used throughout the course.
The access to curricular consultation	A LINE group will be created at the beginning of the semester for course communication.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） 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）
Does the course incorporate AI	AI1：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） AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） AI3：使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
Note