Since 1999, colleges and universities have begun to expand enrollment on a large scale, and correspondingly, postgraduate education has also expanded. With the continuous increase in the number of graduate students, the quality of their education has attracted increasing attention. For graduate students, curriculum is an important carrier of their training and one of the core contents of their education quality assurance. The teaching of ideological and political theory courses for postgraduates is an effective way for postgraduates to impart theoretical knowledge, cultivate moral quality, and stimulate innovation ability. Constructing a scientific and effective new teaching model of ideological and political theory courses for postgraduates in colleges and universities will help to increase the interest and enthusiasm of postgraduates in studying ideological and political theory courses, and help to cultivate innovative talents who are suitable for the needs of the times and develop in an all-round way. The main goal of this paper is to train graduate students with comprehensive development, and aims to study the reform of postgraduate ideological and political education based on machine learning and improved SVM algorithm. This paper proposes machine learning and SVM algorithms, and proposes an improved algorithm based on the previous classic algorithm. The support vector machine model and experiments prove that the improved algorithm is more accurate and faster. The experimental results in this paper show that the improved support vector machine algorithm has the best learning curve, and the improved SVM learning algorithm is found to significantly improve the classification accuracy in the 758 samples taken; when K=16, the correct rate of the picture is as high as it is more than 90%.

Malik Alassery. Postgraduate Teaching Reform Based on Machine Learning and Improved SVM Algorithm. Machine Learning Theory and Practice (2021), Vol. 2, Issue 2: 46-65. https://doi.org/10.38007/ML.2021.020205.

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