机器学习教学改革的实证研究和一些改进建议

Empirical Research on the Reform of Machine Learning Teaching and Some Improvement Suggestions

摘要: 本文主要基于调查问卷，讨论机器学习课程目前存在的问题和针对这些问题的改进措施。存在的问题包括：深度学习系统的分析和设计能力不足，机器学习有关的可视化技术训练不足，课程难度较大，学生对深度学习和神经网络不感兴趣。改进措施包括：加强实践教学环节、增加可视化技术相关教学内容、以更通俗易懂的方式解释复杂概念、增加实际应用案例等。通过这些措施的改进，能够有效的提升机器学习课程的教学质量。

Abstract: This article mainly discusses the current problems in the machine learning curriculum and the improvement measures for these problems based on a questionnaire survey. The problems include: insufficient analysis and design capabilities of deep learning systems, insufficient training in visuali-zation techniques related to machine learning, difficulty in the curriculum, and students’ lack of interest in deep learning and neural networks. The improvement measures include: strengthening practical teaching links, adding teaching content related to visualization techniques, explaining complex concepts in a more understandable way, and adding practical application cases. Through these measures, the teaching quality of the machine learning curriculum can be effectively improved.