课程信息
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初级

完成时间大约为22 小时

建议:5 weeks of study, 2-5 hours/week...

英语(English)

字幕:英语(English)
User
学习Course的学生是
  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Data Analysts
  • Software Engineers

您将获得的技能

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra
User
学习Course的学生是
  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Data Analysts
  • Software Engineers

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

初级

完成时间大约为22 小时

建议:5 weeks of study, 2-5 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 2 小时

Introduction to Linear Algebra and to Mathematics for Machine Learning

5 个视频 (总计 28 分钟), 4 个阅读材料, 3 个测验
5 个视频
Motivations for linear algebra3分钟
Getting a handle on vectors9分钟
Operations with vectors11分钟
Summary1分钟
4 个阅读材料
About Imperial College & the team5分钟
How to be successful in this course5分钟
Grading policy5分钟
Additional readings & helpful references10分钟
3 个练习
Exploring parameter space20分钟
Solving some simultaneous equations15分钟
Doing some vector operations14分钟
2
完成时间为 2 小时

Vectors are objects that move around space

8 个视频 (总计 44 分钟), 4 个测验
8 个视频
Modulus & inner product10分钟
Cosine & dot product5分钟
Projection6分钟
Changing basis11分钟
Basis, vector space, and linear independence4分钟
Applications of changing basis3分钟
Summary1分钟
4 个练习
Dot product of vectors15分钟
Changing basis15分钟
Linear dependency of a set of vectors15分钟
Vector operations assessment15分钟
3
完成时间为 3 小时

Matrices in Linear Algebra: Objects that operate on Vectors

8 个视频 (总计 57 分钟), 3 个测验
8 个视频
How matrices transform space5分钟
Types of matrix transformation8分钟
Composition or combination of matrix transformations8分钟
Solving the apples and bananas problem: Gaussian elimination8分钟
Going from Gaussian elimination to finding the inverse matrix8分钟
Determinants and inverses10分钟
Summary59
2 个练习
Using matrices to make transformations12分钟
Solving linear equations using the inverse matrix16分钟
4
完成时间为 6 小时

Matrices make linear mappings

6 个视频 (总计 53 分钟), 4 个测验
6 个视频
Matrices changing basis11分钟
Doing a transformation in a changed basis4分钟
Orthogonal matrices6分钟
The Gram–Schmidt process6分钟
Example: Reflecting in a plane14分钟
2 个练习
Non-square matrix multiplication20分钟
Example: Using non-square matrices to do a projection12分钟
4.7
678 个审阅Chevron Right

34%

完成这些课程后已开始新的职业生涯

33%

通过此课程获得实实在在的工作福利

来自Mathematics for Machine Learning: Linear Algebra的热门评论

创建者 NSDec 23rd 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

创建者 PLAug 26th 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

讲师

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David Dye

Professor of Metallurgy
Department of Materials
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Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

关于 伦敦帝国学院

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

关于 数学在机器学习领域的应用 专项课程

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....
数学在机器学习领域的应用

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