Mathematics for Machine Learning: Multivariate Calculus

Imperial College London connects calculus with the mathematics of machine learning. The sequence moves through multivariable change, the chain rule and Taylor approximations before introducing optimization and regression. It is a mathematics course with an ML context, useful for learners who want to understand the calculations behind an optimization procedure.

What you will learn

• Describe change in a multivariable function.
• Apply the multivariate chain rule.
• Relate Taylor approximations and gradients to optimization.

Who this suits

Coursera labels this course beginner level. Detailed prerequisites are not specified; preview the algebra, vectors and notation before enrolling.

Before you enroll

Work through the algebra as well as the numerical answer. Sketch the function or region involved and check which assumptions allow the chosen calculus method to apply.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Write a simple two-parameter error function. Sketch its contours and explain how a gradient suggests a direction for reducing the error.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.