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AI Code Generation for Everyone

The Hong Kong University of Science and Technology

AI Code Generation for Everyone

位教师：Kenneth Wai-Ting Leung

包含在中

了解更多

7个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

7个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Use AI tools to generate, modify, and improve code for real-world tasks without prior programming experience.
Apply prompt engineering techniques to guide AI models in producing accurate and useful code.
Evaluate and refine AI-generated code for data analysis, web applications, and visualization projects.

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您将学习的工具

要了解的详细信息

可分享的证书

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有7个模块

This course introduces learners to the practical use of artificial intelligence for generating and improving computer code across multiple disciplines. Designed for students and professionals from any major, it requires no prior programming or AI experience. Learners explore how AI tools support coding tasks through effective prompt design, hands-on experimentation, and guided examples using Python. The course emphasizes real-world applications, including data analysis, basic web development, and research-oriented data visualization. Participants also learn to critically evaluate AI-generated code, identify limitations and errors, and refine outputs for practical use. By the end of the course, learners will be able to responsibly integrate AI-assisted coding into academic, professional, or creative projects, enhancing efficiency, problem-solving skills, and confidence in working with modern AI technologies.

单元详情

Welcome to the course! This topic introduces AI code generation as a transformative approach to software development, where large language models generate code from natural language prompts or contextual cues. Learners examine how AI-assisted coding differs from traditional programming, its historical evolution, practical benefits, limitations, and its growing role in modern development workflows.

涵盖的内容

2个视频6篇阅读材料1个作业

2个视频总计5分钟

Course Introduction2分钟
Introduction to AI Code Generation3分钟

6篇阅读材料总计80分钟

Course Learning Outcomes5分钟
AI Code Generation and Its History20分钟
Traditional vs. AI-Assisted Coding10分钟
AI Code Generation Workflow5分钟
AI Code Generation: Limitations and Concerns10分钟
Common Tools and Platforms & Setting Up Your Environment30分钟

1个作业总计10分钟

Quiz 110分钟

This topic explores prompt engineering as the skill of translating human intent into precise instructions for large language models. Learners study how prompt clarity, specificity, context, and format shape AI outputs, and practice iterative refinement techniques to reliably generate accurate, useful, and ethical code solutions.

涵盖的内容

1个视频5篇阅读材料1个作业

Vibe coding introduces a creative, conversational style of building software by describing goals, aesthetics, and behavior in everyday language while AI handles technical implementation. Learners explore how vibe coding lowers technical barriers, accelerates prototyping, and empowers non-coders and developers alike to rapidly turn ideas into working applications.

涵盖的内容

1个视频4篇阅读材料1个作业

This hands-on module applies AI tools to generate, interpret, and modify Python programs. Learners work with basic syntax, control structures, and logic, using AI to accelerate development while learning to read, test, and debug generated Python code.

涵盖的内容

2个视频2篇阅读材料1个作业1个讨论话题

This topic demonstrates how AI can assist with data mining and data visualization tasks. Learners generate code to collect, analyse, and visualize data, while developing skills to assess data quality, interpret results, and refine AI-generated analytical workflows.

涵盖的内容

2个视频2篇阅读材料1个作业1个讨论话题

This module introduces AI-assisted web development by guiding learners to create a simple personal webpage. Using AI-generated HTML, CSS, and basic interactivity, learners focus on structure, design, and usability while understanding how web components work together.

涵盖的内容

2个视频2篇阅读材料1个作业1个讨论话题

This topic explores AI-assisted coding for research and simulation tasks. Learners use AI to generate code for simulations, experiments, and visualizations, emphasizing interpretation, and presentation of results in academic and exploratory research contexts.