Preliminary study on the reform of machine learning teaching
The machine learning is different from other courses for its large span of mathematical knowledge, widely application of techniques, and fast updating of models.In traditional machine learning classes, due to the separation between review of mathematical knowledge and model explanation, boring conte...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2022-08-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2022026 |
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| Summary: | The machine learning is different from other courses for its large span of mathematical knowledge, widely application of techniques, and fast updating of models.In traditional machine learning classes, due to the separation between review of mathematical knowledge and model explanation, boring content divorced from the reality, and the obsolete content of exam, undergraduates have difficulty in understanding machine learning models.Then they lack interest in learning and also consciousness of autonomous learning, which makes them difficult to solve practical problems with advanced machine learning technologies.Considering these facts, the teaching reform measures of machine learning course were proposed, in terms of teaching methods, content and exam.The teaching methods combined online learning and offline deduction to increase teacher-student interaction and connect key knowledge points.The teaching content introduced scientific stories and interesting challenges, which enriched the content and cultivates learning interest.The advanced machine learning technique-based practice exam was applied to enhance the capability of independent learning and explore advanced technologies.Consequently, the reform measures have been successfully applied in the machine learning teaching practice of Academician Binxing Fang undergraduate preparatory class of Guangzhou University, improving the teaching performance of machine learning course. |
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| ISSN: | 2096-109X |