矩阵论——以数据挖掘与机器学习为例
Matrix Methods and Its Applications in Data Mining and Machine Learning
矩阵理论是一门博大精深的课程，同优化理论、图论等紧密相关，也在目前热门的人工智能中的关键要素之一。本讲义以数据挖掘和机器学习为牵引，立足于介绍矩阵论方面的的基本概念、基本原理，以及主元分析法、奇异值分解、线性方程组求解、随机梯度下降、图神经网络等基本用例，使读者能够快速了解这一交叉领域的内涵。
计算技术、计算机技术
矩阵论研究生课程讲义数据挖掘机器学习人工智能
李荣鹏.矩阵论——以数据挖掘与机器学习为例[EB/OL].(2025-07-19)[2025-11-07].https://chinaxiv.org/abs/202507.00427.点此复制
Matrix theory is a broad and profound course, closely related to optimization theory, graph theory, etc., and is also one of the key elements in the currently popular artificial intelligence. Towards showcasing its applications in data mining and machine learning, this lecture notes introduce the basic concepts and principles of matrix theory, as well as basic use cases such as principal component analysis, singular value decomposition, solving linear equations, stochastic gradient descent, and graph neural networks, so that readers can quickly understand the connotation of this cross-field.
Matrix MethodsLecture NotesData MiningMachine LearningArtificial Intelligence
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