Mathematics for Machine Learning - Linear Algebra - M4ML - Linear Algebra - 1.5 Summary

Welcome to the “Mathematics for Machine Learning: Linear Algebra” course, offered by Imperial College London. Week 1, Video 5 - Summary This video is part of an online specialisation in Mathematics for Machine Learning (m4ml) hosted by Coursera. For more information on the course and to access the full experience, please visit: https://www.coursera.org/specializations/mathematics-machine-learning Full Playlist - https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3 Your course instructors are - Dr David Dye (@DavidDye9, https://twitter.com/DavidDye9) - Dr Sam Cooper (@camsooper, https://twitter.com/camsooper) If you have any questions about the course, please contact the instructors via Twitter. This course offers an introduction to the linear algebra required for common machine learning techniques. We start by looking at some simultaneous equations problems and showing how these can be expressed using vectors and matrices. We then move on to exploring vector spaces and see how these can be reformulated by changing basis. Next, we explore some methods for manipulating matrices and see how this is done using code, before moving on to some special cases shown using interactive animations. In the final module, we bring all the concepts together to recreate Google’s famous PageRank algorithm, which uses eigenvectors to rank search result by their connectivity. This course was designed to help you quickly build an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck; it is not intended cover all the details. We hope you enjoy it and that it gives you the confidence to dive into one of the many other wonderful machine learning courses available online!