/mathematics-of-machine-learning-linear-algebra

Programming Assignments for Imperial College London's Mathematics of Machine Learning: Linear Algebra course.

Primary LanguageJupyter NotebookMIT LicenseMIT

Table of Contents

Overview

NOTE: This repository is for learning purposes only. Please follow the Coursera honor code. I've posted the answers here with the intent that it helps with debugging your own code. I encourage you to utilize the discussion forums available via Coursera and use this repo to understand why your program isn't working as expected. Best of luck!

Here's a peak into what you'll learn in the course! Apply a matrix transformation to the coordinates of each of the pixels in an image to transform the image as a whole. Given a starting image (such as this one of “The Ambassadors” [1533] by Hans Holbein the Younger), which is made up of 400×400 pixels, if we apply the same transformation to each of those 160,000 pixels, what will the transformed image become?

Objectives

Week 1: Recall how machine learning and vectors and matrices are related. Interpret how changes in the model parameters affect the quality of the fit to the training data.

Week 2: Calculate basic operations (dot product, modulus, negation) on vectors. Calculate a change of basis.

Week 3: Understand what a matrix is and how it corresponds to a transformation. Explain and calculate inverse and determinant of matrices.

Week 4: Identify matrices as operators. Relate the transformation matrix to a set of new basis vectors.

Week 5: Identify geometrically what an eigenvector/value is. Apply mathematical formulation in simple cases. Build an intuition of larger dimention eigensystems

Assignments