Mathematics for Machine Learning Specialization

开始时间: 05/14/2018 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 maths - 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 specialisation aims to bridge that gap, getting you up to speed in the underlying maths, 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 optimise 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 Components Analysis, uses the maths from the first two courses to do simple optimisation for the situation where you don’t have an understanding of how the data variables relate to each other. At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.


3 courses

Mathematics for Machine Learning: Linear Algebra
Current session: May 14
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, an

Mathematics for Machine Learning: Multivariate Calculus
Current session: May 14
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting thi

Mathematics for Machine Learning: PCA
Upcoming session: May 28
This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll


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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