矩阵论——以数据挖掘与机器学习为例

Краткое изложение: 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.