查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

113-2 341816 人工智慧概論與計算機程式 1 2.0 3 ▲ 李旻璁工程一 60 0 計中電腦教室

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
113-2	341816	人工智慧概論與計算機程式	1	2.0	3	▲	李旻璁	工程一	60	0	計中電腦教室

教學大綱與進度：

教師姓名李旻璁　教師諮商時間(Office Hours)

Email mtlee@ntut.edu.tw

最後更新時間 2024-12-16 11:04:33

課程大綱 This course emphasizes understanding the algorithms behind programming solutions, rather than focusing solely on mastering a specific programming language. You will learn how to conceptualize, develop, and implement algorithms to solve engineering problems, with a dual-language approach to demonstrate versatility. Lectures will utilize Fortran to teach algorithmic fundamentals, while Python will be used to replicate tasks, showcasing how the same logic translates across languages. An integral component of this course is leveraging Generative AI tools, such as ChatGPT, GitHub Copilot, and Replit, to aid in code generation, validation, and exploration of new programming paradigms. Students will develop skills to critically evaluate AI-generated code, refine it to meet their objectives, and learn new coding languages by building upon existing knowledge. Through this approach, you will gain confidence in creating robust and adaptable solutions for engineering challenges.

課程進度 Part I: Theory and Tools (2-3 weeks) 1. Introduction to Programming and GenAI Tools: Overview of the importance of programming in engineering, introduction to Generative AI tools like ChatGPT, GitHub Copilot, and Replit, and their role in coding. Discussing the choice of programming languages and fundamental programming concepts. 2. Development Environment: Introduction to the Linux platform, vim editor, compilers, interpreters, and data visualization tools such as gnuplot. Focus on data collection, analysis, and visualization techniques. Part II: Fundamentals of Programming (7 weeks) 1. Input and Output: Techniques for reading from and writing to different data sources. 2. Variables and Declaration: Understanding data types, variable declaration, and scope. 3. Mathematical Functions: Utilizing built-in math functions for engineering calculations. 4. Control Structures: Implementing loops (do loops) and conditional statements (if statements) for flow control. 5. Arrays and Data Handling: Managing and manipulating arrays for efficient data storage and retrieval. 6. Subroutines and Modules: Creating reusable code with subroutines and modules; introduction to random number generation. Part III: Algorithm Development (4-6 weeks) 1. Data Sorting: Techniques and algorithms for sorting data effectively. 2. Statistical Analysis: Implementing algorithms for statistical computations and data interpretation. 3. Probability and Histograms: Coding solutions for probability calculations and histogram generation. 4. Regression Analysis: Developing algorithms for regression analysis to model and predict data trends.

評量方式與標準 class participation: 60% In-class quizzes (x4): (40%) There is no midterm and final in this course. Students need to attend the class in person and answer questions on Zuvio to get scores for class participation.

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
Online resources.

課程諮詢管道 email / Zuvio

課程對應SDGs指標 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）

課程是否導入AI

備註

教師姓名	李旻璁　教師諮商時間(Office Hours)
Email	mtlee@ntut.edu.tw
最後更新時間	2024-12-16 11:04:33
課程大綱	This course emphasizes understanding the algorithms behind programming solutions, rather than focusing solely on mastering a specific programming language. You will learn how to conceptualize, develop, and implement algorithms to solve engineering problems, with a dual-language approach to demonstrate versatility. Lectures will utilize Fortran to teach algorithmic fundamentals, while Python will be used to replicate tasks, showcasing how the same logic translates across languages. An integral component of this course is leveraging Generative AI tools, such as ChatGPT, GitHub Copilot, and Replit, to aid in code generation, validation, and exploration of new programming paradigms. Students will develop skills to critically evaluate AI-generated code, refine it to meet their objectives, and learn new coding languages by building upon existing knowledge. Through this approach, you will gain confidence in creating robust and adaptable solutions for engineering challenges.
課程進度	Part I: Theory and Tools (2-3 weeks) 1. Introduction to Programming and GenAI Tools: Overview of the importance of programming in engineering, introduction to Generative AI tools like ChatGPT, GitHub Copilot, and Replit, and their role in coding. Discussing the choice of programming languages and fundamental programming concepts. 2. Development Environment: Introduction to the Linux platform, vim editor, compilers, interpreters, and data visualization tools such as gnuplot. Focus on data collection, analysis, and visualization techniques. Part II: Fundamentals of Programming (7 weeks) 1. Input and Output: Techniques for reading from and writing to different data sources. 2. Variables and Declaration: Understanding data types, variable declaration, and scope. 3. Mathematical Functions: Utilizing built-in math functions for engineering calculations. 4. Control Structures: Implementing loops (do loops) and conditional statements (if statements) for flow control. 5. Arrays and Data Handling: Managing and manipulating arrays for efficient data storage and retrieval. 6. Subroutines and Modules: Creating reusable code with subroutines and modules; introduction to random number generation. Part III: Algorithm Development (4-6 weeks) 1. Data Sorting: Techniques and algorithms for sorting data effectively. 2. Statistical Analysis: Implementing algorithms for statistical computations and data interpretation. 3. Probability and Histograms: Coding solutions for probability calculations and histogram generation. 4. Regression Analysis: Developing algorithms for regression analysis to model and predict data trends.
評量方式與標準	class participation: 60% In-class quizzes (x4): (40%) There is no midterm and final in this course. Students need to attend the class in person and answer questions on Zuvio to get scores for class participation.
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 Online resources.
課程諮詢管道	email / Zuvio
課程對應SDGs指標	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）
課程是否導入AI
備註