Mathematics for Machine Learning: Linear Algebra

Start Date: 07/05/2020

 Course Type: Common Course

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.

Course Syllabus

In this first module we look at how linear algebra is relevant to machine learning and data science. Then we'll wind up the module with an initial introduction to vectors. Throughout, we're focussing on developing your mathematical intuition, not of crunching through algebra or doing long pen-and-paper examples. For many of these operations, there are callable functions in Python that can do the adding up - the point is to appreciate what they do and how they work so that, when things go wrong or there are special cases, you can understand why and what to do.

Course Introduction

Mathematics for Machine Learning: Linear Algebra Mathematics for Machine Learning is the entry point to understanding any linear algebra problem. We assume that you have already acquired basic knowledge in computer science, and that you are willing to take a small mathematical test. We hope that you will take it; otherwise, you will probably get annoyed and frustrated. In this course, we will build a linear model for an input vector x such that a(x) < b(x) We assume that we have determined the linear relationship between a and b. We will assume that we have also determined the conservation of mass, and that we have used vector calculus. We will assume that we have also learned the necessary calculus, as discussed in the course material. If you have not, please take a look at our introductory course on linear algebra and vector spaces. If you have a basic knowledge in linear algebra, you will be able to follow along with the course material. If not, please check out our other courses on linear algebra and data science. When we first introduce the class, we assume that you already have these prerequisites. If not, please read our disclaimer at the beginning of this course. Linear models are very powerful machine learning algorithms. They allow us to solve complex machine learning problems very quickly, and in many cases, we can use them to introduce new problems. In this course, we will focus on applications of linear models, introducing you to the basics of

Course Tag

Machine Learning Mathematics for Machine Learning Linear Algebra Math Eigenvalues And Eigenvectors Basis (Linear Algebra) Transformation Matrix

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