Abstract
Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.
Highlights
In near few years, some new-generation information technologies are proposed, such as cloud computing, Internet of ings technology, human-computer interaction, and mobile Internet
Such courses mainly include advanced mathematics, linear algebra, data structure, algorithm design and analysis, probability theory and mathematical statistics, machine learning, artificial intelligence, and other professional basic courses related to machine learning. ese courses are frequently extremely professional and theoretical, and students frequently fall behind in following professional courses as a result of inadequate early mastery of advanced mathematics, linear algebra, and other mathematical concepts
Aiming at the shortcomings of traditional professional experiments and combining the characteristics of machine learning courses, we propose the following suggestions: (1) From the beginning of the course, teachers should focus on cultivating students’ hands-on practical ability, combining theory with practice, and increasing the proportion of experimental classes
Summary
Some new-generation information technologies are proposed, such as cloud computing, Internet of ings technology, human-computer interaction, and mobile Internet. Scientific Programming is the development of machine learning courses Such courses mainly include advanced mathematics, linear algebra, data structure, algorithm design and analysis, probability theory and mathematical statistics, machine learning, artificial intelligence, and other professional basic courses related to machine learning. Ese courses are frequently extremely professional and theoretical, and students frequently fall behind in following professional courses as a result of inadequate early mastery of advanced mathematics, linear algebra, and other mathematical concepts To address these issues, this article will develop a new teaching mode, track students’ learning situations intelligently, and alter the learning state as needed. Students’ facial expression data is collected, and classification choices are made using classification algorithms. e quality of classroom instruction may be correctly fed back based on the outcome of the choice
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