Mathematics in Machine Learning: the Foundation of Intelligent Systems

EasyChair Preprint 15608

Abstract

Machine Learning (ML) has become a transformative technology, impacting various fields such as healthcare, finance, and robotics. At its core, ML is deeply rooted in mathematical concepts, enabling the development of models that can learn from data and make predictions. This paper delves into the essential role of mathematics in ML, covering the foundational principles, key techniques, and advanced methodologies that propel the field forward. By examining linear algebra, calculus, probability, and optimization, we aim to provide a comprehensive understanding of how mathematics serves as the backbone of machine learning algorithms.

Keyphrases: Algorithms, Optimization, machine learning, math

@booklet{EasyChair:15608,
  author    = {Chi Zhang and Ye Zhou and Ken Yamada},
  title     = {Mathematics in Machine Learning: the Foundation of Intelligent Systems},
  howpublished = {EasyChair Preprint 15608},
  year      = {EasyChair, 2024}}