机器学习课程的教学设计与实践——以逻辑回归为例

Teaching Design and Practice of Machine Learning Courses—A Case Study of Logistic Regression

摘要: 机器学习是计算机科学及相关领域的重要基础课程，也是培养学生应用数学知识解决实际问题能力的关键课程。该课程的内容具有严谨的理论基础、复杂的数据处理过程和广泛的实际应用性。通过学习机器学习，能够帮助学生掌握数据分析和模型建立的核心方法，提升学生的抽象建模、算法推理以及创新应用的能力，同时培养学生提出问题、验证假设和解决复杂问题的能力。结合多年的教学实践经验，文章以逻辑回归算法为例，探讨了机器学习课程的教学设计与实施策略，重点分析了如何通过案例驱动的教学方法帮助学生更好地理解和掌握算法原理与应用技巧。通过对教学内容、教学方法以及评估手段的深入讨论，旨在为提升机器学习课程的教学效果提供理论依据和实践指导。

Abstract: Machine learning is a fundamental course in computer science and related fields, and it plays a critical role in developing students’ ability to apply mathematical knowledge to solve real-world problems. The course content is characterized by a rigorous theoretical foundation, complex data processing procedures, and broad practical applications. Through the study of machine learning, students can master core methods for data analysis and model construction, enhancing their abilities in abstract modeling, algorithmic reasoning, and innovative applications, while also fostering their skills in problem formulation, hypothesis testing, and solving complex problems. Drawing on years of teaching experience, this paper uses the logistic regression algorithm as a case study to explore the teaching design and implementation strategies for machine learning courses. It focuses on how case-driven teaching methods can help students better understand and master the principles of algorithms and application techniques. Through an in-depth discussion of the teaching content and instructional methods, this paper aims to provide a theoretical basis and practical insights for improving the teaching effectiveness of machine learning courses.