Miquel Noguer Alonso: The Mathematics of AI, Gebunden

Klappentext

Explore the mathematical foundations driving artificial intelligence and generative AI

The Mathematics of AI: Theory and Application for Investors by Miquel Noguer Alonso provides a rigorous examination of the mathematical principles underlying modern artificial intelligence. From linear algebra and probability theory to optimization and statistical learning, this book connects foundational mathematical concepts directly to practical AI applications, offering readers a structured path through the quantitative reasoning that powers today's most advanced generative models and algorithms.

The Mathematics of AI covers the core mathematical building blocks that drive machine learning and generative AI systems. Readers gain a thorough grounding in the theoretical frameworks behind neural networks, deep learning architectures, and generative modeling techniques. Each topic is presented with the analytical depth needed to understand how mathematical structures translate into functioning AI systems used across finance and other industries.

Readers will also find:

• Detailed coverage of linear algebra, calculus, and probability as they apply directly to modern machine learning algorithms

• A rigorous treatment of the optimization methods that are central to training neural networks and deep learning architectures

• Thorough analysis of generative model mathematics, including the underlying theory behind variational autoencoders and related techniques

• Practical connections between abstract mathematical frameworks and real-world artificial intelligence applications across the finance industry

• A carefully structured progression from foundational mathematical concepts through to advanced topics in modern generative AI

The Mathematics of AI is written for quantitative professionals, data scientists, financial engineers, and advanced students seeking a rigorous mathematical understanding of artificial intelligence. It serves anyone who needs to move beyond surface-level AI knowledge into the quantitative depth that underpins modern generative systems.