Mathematical Analysis of Machine Learning Algorithms

The mathematical theory of machine learning explains current algorithms and motivates principled approaches for the future. This textbook introduces students and researchers to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include supervised learning, neural networks, and sequential decision-making. Readers with background knowledge of basic machine learning methods will gain sufficient technical knowledge to understand research papers in theoretical machine learning.

Format: Hardback
Length: 479 pages
Publication date: 31 July 2023
Publisher: Cambridge University Press

Machine learning is a rapidly evolving field that has the potential to revolutionize the way we interact with technology. The mathematical theory of machine learning plays a crucial role in understanding and developing new algorithms. This self-contained textbook provides an introduction to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

Supervised learning algorithms, which are used to analyze and predict data based on labeled examples, are discussed in detail. The analysis of neural networks, which are modeled as mathematical functions that compute outputs based on input data, is also covered. Neural tangent kernel and mean-field analysis are two important techniques used to understand the behavior of neural networks.

Machine learning algorithms are used in a wide range of applications, including online learning, bandit problems, and reinforcement learning. Online learning involves learning from data sequentially, while bandit problems involve making decisions in the presence of uncertainty. Reinforcement learning involves learning from feedback and adapting to changing environments.

Students who study this textbook will gain a deep understanding of the mathematical tools used in the theoretical analysis of machine learning problems. They will also learn how to apply these tools to analyze various concrete algorithms, such as support vector machines, decision trees, and neural networks.

This textbook is ideal for readers who have some background knowledge of basic machine learning methods but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning. The book is well-written and accessible, making it a valuable resource for students, researchers, and practitioners in the field.

In conclusion, the mathematical theory of machine learning is a powerful tool that can help us understand and develop new algorithms. This self-contained textbook provides an excellent introduction to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. By studying this textbook, students will gain a deep understanding of the mathematical tools used in the theoretical analysis of machine learning problems and how to apply them to analyze various concrete algorithms.

Weight: 1046g
Dimension: 185 x 264 x 32 (mm)
ISBN-13: 9781009098380