About the book
This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Editions of Mathematics for Machine Learning
- Hardcover
- ISBN 9781108470049
- Paperback
- ISBN 9781108455145
Read an Excerpt
This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Frequently Asked Questions
What is Mathematics for Machine Learning about?
This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
What core themes, tropes, or subjects are explored in Mathematics for Machine Learning?
Computing and Information Technology > Computer science > Artificial intelligence (AI) > Machine learning
Where can I read a sample of Mathematics for Machine Learning?
You can read an official preview of the few pages here https://www.book2look.com/book/9781108470049
Who is/are the Author/s of the book Mathematics for Machine Learning?
Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
What are the ISBN numbers for the physical and digital editions?
Mathematics for Machine Learning is available as hardcover(ISBN 9781108470049) and paperback(ISBN 9781108455145)