Eigenvalues And EigenvectorsPrincipal Component Analysis (PCA)Multivariable CalculusLinear AlgebraBasis (Linear Algebra)Transformation MatrixLinear RegressionVector CalculusGradient DescentPython ProgrammingProjection MatrixMathematical Optimization
数学在机器学习领域的应用 专项课程
数学在机器学习领域的应用. Learn about the prerequisite mathematics for applications in data science and machine learning
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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.
应用的学习项目
Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.
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初级
无需相关领域的预备知识无需相关经验。
完成时间大约为2 个月
建议 13 小时/周
英语（English）
字幕：英语（English）, 希腊语, 西班牙语（Spanish）
专项课程的运作方式
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在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 3 门课程
Mathematics for Machine Learning: Linear Algebra
5,029 个评分
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.
Mathematics for Machine Learning: Multivariate Calculus
2,601 个评分
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 this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
Mathematics for Machine Learning: PCA
1,378 个评分
This intermediate-level 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 compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
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.
审阅
4.5
来自数学在机器学习领域的应用的热门评论
由 GK 提供Mar 7, 2019
Taught in an intuitive way. Never have I been able to understand linear algebra better than after following the first 3 weeks of this course. I can't wait to complete the entire specialization
由 MI 提供Jul 6, 2018
Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.
由 GE 提供Jul 21, 2019
Another great course from Imperial College London. I highly recommend this specialization.
由 GE 提供Jul 26, 2019
I have thoroughly enjoyed every course of this specialization. Thank you very much.
由 VM 提供Sep 11, 2018
It was a good course compared to other two courses of this specialization.
由 SS 提供Aug 3, 2019
Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.
由 EC 提供Sep 9, 2019
Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.
由 JT 提供Nov 12, 2018
Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.
常见问题
退款政策是如何规定的？
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可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。
有助学金吗？
我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程需要多长时间？
3/4 hours a week for 3 to 4 months
What background knowledge is necessary?
High school maths knowledge is required. Basic knowledge of Python can come in handy, but it is not necessary for courses 1 and 2. For course 3 (intermediate difficulty) you will need basic Python and numpy knowledge to get through the assignments.
Do I need to take the courses in a specific order?
We recommend taking the courses in the order in which they are displayed on the main page of the Specialization.
完成专项课程后我会获得大学学分吗？
This is a non-credit Specialization.
What will I be able to do upon completing the Specialization?
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.
还有其他问题吗？请访问 学生帮助中心。
