Mathematics for Machine Learning: Linear Algebra

开始时间: 03/28/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew



Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.


第一个写评论        关注课程


In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

机器学习数学:线性代数:在本课程中,我们研究线性代数是什么以及它与向量和矩阵的关系。然后,我们研究向量和矩阵是什么以及如何使用它们,包括特征值和特征向量的棘手问题,以及如何使用它们来解决问题。最后,我们看一下如何使用它们来处理数据集,例如如何旋转面部图像以及如何提取特征向量以查看Pagerank算法的工作原理。 由于我们的目标是数据驱动的应用程序,因此我们将在代码中实现其中一些想法,而不仅仅是在纸上。在课程结束时,您将编写代码块并使用Python来阅读Jupyter笔记本,但是不用担心,这些内容会很简短,侧重于概念,并且如果您以前没有编码,则会指导您完成操作。 在本课程的最后,您将对向量和矩阵有一个直观的了解,这将帮助您弥合差距到线性代数问题,以及如何将这些概念应用于机器学习。





In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and


数据科学 机器学习 PCA 多变量微积分 微积分 帝国理工学院 主成分分析 线性代数 数学 机器学习数学基础 面向机器学习的数学 伦敦帝国理工学院