“机器学习”案例化教学实践与探讨——以贝叶斯分类器为例

Case-based Teaching Practice and Discussion on Machine Learning:Taking Bayesian Classifier as an Example

《机器学习》教学内容理论深、算法多、难度大,难以理解,造成学习兴趣难以维持和提高,采用案例化的教学方法是改善这一困境的有益尝试。该过程可让学生从实际场景入手,由浅入深,逐步引导学生解决问题,既巩固已学理论知识,又让学生掌握新课程内容,激发学生的积极性和参与度。给出一个贝叶斯分类器案例教学过程,实践证明,该方法能够有效地帮助学生掌握贝叶斯分类器的分类过程及实际应用现状,并为他们以后的工作打下基础。

The teaching content of Machine Learning course involves various deep and difficult theory,especially many algorithms,which makes it difficult for students to understand and improve learning interest.Case teaching method is a more effective way for this course.It enables students to start from the actual case,helps step by step,and gradually guides students to solve problems.It not only consolidates what they have learned,but also helps students to master new contents and stimulates students'enthusiasm and participation.A Bayesian classifier case is applied in this course teaching.It has been proved that it effectively helps students master the classification process and practical application of Bayesian classifiers,and lays a foundation for future work.