Mathematics for AI
Linear Algebra, Calculus, Probability and Optimisation
About this guide
A concise exam companion for the mathematics behind AI. It covers vectors and matrices, linear systems, eigenvalues and singular value decomposition, multivariable calculus and gradients, probability, random variables and distributions, statistics and maximum likelihood, optimisation and gradient descent, and information theory. Worked examples are marked the way an examiner marks them, so you can see why an answer earns the grade.
Inside the guide
- Matrices
- Linear systems
- Eigenvalues and singular value decomposition
- Multivariable calculus and gradients
- Probability
- Random variables and distributions
- Statistics and maximum likelihood
- Optimisation and gradient descent
- Information theory
Questions students ask
What does Mathematics for AI cover?
It covers vectors and matrices, linear systems, eigenvalues and singular value decomposition, multivariable calculus and gradients, probability, random variables and distributions, statistics and maximum likelihood, optimisation and gradient descent, and information theory.
Who is Mathematics for AI for?
It is written for university students revising the mathematics behind AI, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I get a free copy?
Yes. Full Marks Press offers free review copies to students and educators at fullmarkspress.com/free.