Mathematics for Machine Learning Specialization

开始时间: 10/10/2020 持续时间: Unknown

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew



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For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

机器学习专业的数学:对于许多机器学习和数据科学的更高级别的课程,您会发现需要重新学习数学的基础知识-您以前在学校或大学曾学过的东西,但是在另外一门课程中曾讲过上下文,或者不是很直观,因此您很难将其与计算机科学中的用法相关联。该专业旨在弥合这一差距,让您快速掌握基础数学,建立直觉的理解并将其与机器学习和数据科学联系起来。 在有关线性代数的第一门课程中,我们了解什么是线性代数以及它与数据的关系。然后,我们研究什么是向量和矩阵以及如何使用它们。 第二门课程,多元演算,以此为基础,着眼于如何优化拟合函数以获得与数据的良好拟合。它从入门演算开始,然后使用第一个过程中的矩阵和向量来查看数据拟合。 第三个课程是使用主成分分析进行降维,它使用前两个课程中的数学来压缩高维数据。这门课程是中等难度的,需要Python和numpy知识。 在本专业课程结束时,您将获得必备的数学知识,以继续您的旅程并参加机器学习的高级课程。


Mathematics for Machine Learning: Linear Algebra
Mathematics for Machine Learning: Multivariate Calculus
Mathematics for Machine Learning: PCA


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伦敦帝国理工学院的面向机器学习的数学专项课程系列(Mathematics for Machine Learning Specialization),该系列包含3门子课程,涵盖线性代数,多变量微积分,以及主成分分析(PCA),这个专项系列课程的目标是弥补数学与机器学习以及数据科学鸿沟,感兴趣的同学可以关注:Mathematics for Machine Learning。Learn about the prerequisite mathematics for applications in data science and machine learning


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