Mathematics for Machine Learning: Linear Algebra, Module 2 Vectors are objects that move around space

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Mathematics for Machine Learning: Linear Algebra:

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About this 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.

Who is this class for: This course is for people who want to refresh their maths skills in linear algebra, particularly for the purposes of doing data science and machine learning, or learning about data science and machine learning. We look at vectors, matrices and how to apply these to solve linear systems of equations, and how to apply these to computational problems.

Created by: Imperial College London

Module 2 Vectors are objects that move around space

In this module, we look at operations we can do with vectors – finding the modulus (size), angle between vectors (dot or inner product) and projections of one vector onto another. We can then examine how the entries describing a vector will depend on what vectors we use to define the axes – the basis. That will then let us determine whether a proposed set of basis vectors are what’s called ‘linearly independent.’ This will complete our examination of vectors, allowing us to move on to matrices in module 3 and then start to solve linear algebra problems.

Less

Learning Objectives

• Calculate basic operations (dot product, modulus, negation) on vectors

• Calculate a change of basis

• Recall linear independence

• Identify a linearly independent basis and relate this to the dimensionality of the space

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