Mathematics in Machine Learning: the Foundation of Intelligent Systems

EasyChair Preprint 15507

Abstract

Machine Learning (ML) has emerged as a transformative technology, influencing diverse fields such as healthcare, finance, and robotics. At its core, ML relies heavily on mathematical concepts to develop models capable of learning from data and making predictions. This paper explores the critical role of mathematics in ML, discussing the foundational principles, key techniques, and advanced methodologies that drive the field forward. Through an examination of linear algebra, calculus, probability, and optimization, we aim to provide a comprehensive understanding of how mathematics forms the backbone of machine learning algorithms.

Keyphrases: Algorithms, Optimization, machine learning, math

@booklet{EasyChair:15507,
  author    = {Samul Tick and Maria Kin and Lilyana Starlingford and James Kan and Li Wei and Mo Zhang and Mehmmet Amin},
  title     = {Mathematics in Machine Learning: the Foundation of Intelligent Systems},
  howpublished = {EasyChair Preprint 15507},
  year      = {EasyChair, 2024}}