机器学习算法教学实践与探索：以线性回归为例
Teaching Practice and Exploration of Machine Learning Algorithms: A Case Study of Linear Regression

随着当今人工智能技术的迅速发展和广泛应用，机器学习作为其重要组成部分彰显出关键性作用。然而，机器学习领域的知识涵盖范围广泛、更新快速且难度较大，给教师和学生带来了巨大挑战，特别是针对非计算机专业的经管类本科生，缺乏针对性的教学经验。本文以线性回归为例，探讨了一种融合理论与实践的机器学习算法教学方法。通过简洁的理论讲解算法原理，然后引导学生运用实际数据进行操作，涵盖数据预处理、模型训练和评估等步骤，这种方法有助于学生从被动地接受知识转变为主动学习并参与实践，深化对算法的理解，也有助于机器学习课程的设计与改进。
With the rapid development and widespread application of today’s artificial intelligence technol-ogy, machine learning plays a crucial role as a significant component. However, the field of ma-chine learning encompasses a wide range of knowledge, updates swiftly, and presents substantial challenges for both educators and students. This challenge is particularly pronounced for non- computer science majors, such as business and management undergraduate students, who lack tailored teaching experience. This paper takes linear regression as an example to explore a blended approach to teaching machine learning algorithms that integrates theory and practice. Through concise theoretical explanations of algorithm principles, students are guided to apply practical data and cover steps like data preprocessing, model training, and evaluation. This method facilitates the transformation of students from passive knowledge recipients to active participants in practical applications, thereby deepening their understanding of algorithms. It also holds potential for the design and enhancement of machine learning courses.