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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/matrix-methods
课程评论：没有评论
课程名称：矩阵方法 课程概述：数学矩阵方法是机器学习和表格数据分析大多数方法的基础。您将学习矩阵方法的基础知识，包括矩阵之间的乘法、线性方程的求解、正交性和最佳最小二乘逼近。课程中还将探讨奇异值分解（Singular Value Decomposition），这在降维、主成分分析（Principal Component Analysis）和降噪中起着重要作用。课程提供可选的Python示例，以帮助学习者理解概念并实验算法。 课程大纲： 1. 矩阵作为数学对象 2. 矩阵乘法及其他操作 3. 线性方程组 4. 线性最小二乘法 5. 奇异值分解 此课程适合希望深入了解矩阵在数据分析和机器学习中的应用的学习者。
Name:Matrices as Mathematical Objects
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Name:Matrix Multiplication and other Operations
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Name:Systems of Linear Equations
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Name:Linear Least Squares
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Name:Singular Value Decomposition
Description:
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
