返回到 Mathematics for Machine Learning: Linear Algebra
学生对 Imperial College London 提供的 Mathematics for Machine Learning: Linear Algebra 的评价和反馈
12,542 个评分
课程概述
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.
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GB
Aug 16, 2020
The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.
LK
Oct 26, 2023
Very good course. I liked very much the way the topics were presented and explained. I especially appreciate David Dye's clarity of explanations, enthusiasm, passion, and joyful attitude. Thank you.
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2476 - Mathematics for Machine Learning: Linear Algebra 的 2478 个评论（共 2,478 个）
创建者 Inderjot S
•Oct 26, 2025
waste of time and resources
创建者 Enyang W
•Aug 23, 2019
worst course ever
创建者 Vaibhav J
•Aug 9, 2020
Bad