Mathematics for Machine Learning Specialization
Course Features
Duration
4 months
Delivery Method
Online
Available on
Limited Access
Accessibility
Desktop, Laptop
Language
English
Subtitles
English
Level
Beginner
Effort
4 hours per week
Teaching Type
Self Paced
Course Description
Course Overview
International Faculty
Case Based Learning
Post Course Interactions
Case Studies,Hands-On Training,Instructor-Moderated Discussions
Skills You Will Gain
What You Will Learn
Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Master PCA
Course Content
Module 1: Mathematics for Machine Learning: Linear Algebra
1. 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 Instructors
David Dye
Professor of Metallurgy
Samuel J. Cooper
Associate Professor
A. Freddie Page
Strategic Teaching Fellow