Course Code: AIB523
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Synopsis

Artificial intelligence (AI) has been transforming the way everyone lives, studies, works, and connects. This course AIB523 Python for AI is designed to equip students with the knowledge in AI in order to embrace the technological revolution and paradigm shift. Python is an essential programming language in the toolkit of an AI professional. In this course, you will learn the essentials of Python programming in data management, data analytics, and data visualisation. By the end of the course, you should be able to understand the concepts of machine learning and deep learning and differentiate supervised and unsupervised learning. Next, students will learn to execute and implement AI models (e.g., regression and classification) to solve real-life problems. Last, examples and hands-on exercises will be designed to help students learn to visualise and present the machine learning results using Python toolkits, e.g., NumPy, SciPy, Pandas, Seaborn and Matplotlib. 人工智能（AI）正在改变人们的生活方式、学习方式、工作方式和联系方式。本课程AIB523人工智能Python基础旨在赋予学生AI知识，以拥抱技术革命和范式转变。Python是AI专业人士工具包中的基本编程语言。在本课程中，学生将学习Python编程在数据管理、数据分析和数据可视化中的基础知识。通过本课程，学生将能够理解机器学习和深度学习的概念，区分监督学习和无监督学习。另外，学生将学习执行和实现AI模型（例如回归和分类）以解决实际问题。最后，我们将设计示例和动手练习，帮助学生学会使用Python工具包（例如NumPy、SciPy、Pandas、Seaborn和Matplotlib）可视化和展示机器学习结果。
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Introduction to Python programming (basic methodology, syntax, logic, etc.) Python 编程简介（基本方法、语法、逻辑等）
  • Data types (tuple, list, dictionary) 数据类型（元组、列表、字典）
  • Numpy basics (array, module, method, function) Numpy 基础（数组、模块、方法、函数）
  • Pandas and dataframe Pandas 和数据框架
  • Data collection (importing and storing data, web scraping, etc.) 数据收集（导入和存储数据、网页抓取等）
  • Data preparation (manipulation, cleaning, transforming, merging, etc.) 数据准备（操作、清理、转换、合并等）
  • Plotting and visualisation 绘图和可视化
  • Regression vs classification 回归 vs 分类
  • Unsupervised learning 无监督学习
  • Artificial neural network 人工神经网络
  • Convolutional neural network 卷积神经网络
  • Python for NLP basics (tokenisation, stemming, lemmatisation) Python 自然语言处理基础（分词、词干提取、词形还原）

Learning Outcome

  • Appraise the fundamental methodology in Python programming 评估Python编程的基本方法论
  • Evaluate supervised and unsupervised learning, regression and classification problems评估监督学习和无监督学习、回归和分类问题
  • Assess the applications for AI models评估AI模型的应用
  • Prepare data for analysis using Python 使用Python清理准备数据
  • Analyse data using appropriate tools and techniques with Python 使用Python的适当工具和技术分析数据
  • Design and implement various AI models using Python 使用Python设计和实现各种AI模型
Part-time Programmes