Mathematics in machine learning
Please sign in so that we can notify you about a reply
The fundamental mathematical disciplines necessary for understanding machine learning are linear algebra, analytical geometry, vector analysis, optimization, probability theory, and statistics. Traditionally, all these topics are spread across different courses, making it difficult for students studying data science or computer science, as well as professionals in machine learning, to integrate their knowledge into a unified concept.
This book is self-contained: the reader is introduced to basic mathematical concepts and then moves on to four main machine learning methods: linear regression, principal component analysis, Gaussian modeling, and support vector machines.
For those who are just starting to study mathematics, this approach will help develop intuition and gain practical experience in applying mathematical knowledge. For readers with a basic mathematical background, the book will serve as a starting point for a more advanced exploration of machine learning.
Author:
Author:Daisenrot Mark Peter
Cover:
Cover:soft
Category:
- Category:Computer & Technology
- Category:Science & Math
Dimensions:
Dimensions:23.5x16.5x2.7 cm
Series:
Series:For professionals
ISBN:
ISBN:978-5-4461-1788-8
No reviews found