Название: Before Machine Learning Volume 3 - Probability and Statistics for A.I: The fundamental mathematics for Data Science
Автор: Jorge Brasil
Издательство: Independently published
Год: 2024
Страниц: 385
Язык: английский
Формат: pdf (true)
Размер: 17.9 MB

What happens when the world’s greatest diamond heist meets the world of probability and statistics? In this captivating and conversational guide, the 2003 Antwerp Diamond Heist serves as a rich backdrop to explain the core concepts of probability and statistics for Artificial Intelligence (AI). Through the lens of the heist, we’ll explore the deeper workings of Bayesian statistics, Markov Chains, and other powerful techniques, all while uncovering how these ideas apply to modern AI.

This book is the last volume of a three-volume series called Before Machine Learning. The first two volumes, which cover linear algebra and calculus, are available on Amazon or my website.
Before Machine Learning Volume 1 - Linear Algebra for A.I.: The fundamental mathematics for Data Science and Artificial Inteligence
Before Machine Learning, Volume 2 - Calculus for A.I.: The fundamental mathematics for Data Science and Artificial Intelligence

Machine Learning is fundamentally about vast, complex, and often messy data. To transform this raw data into actionable insights, we must navigate uncertainty, discern patterns, and make informed predictions. This is where probability and statistics become crucial. They provide the mathematical foundation for making sense of data, allowing us to draw meaningful, reliable conclusions.

Linear algebra equips us with the tools to represent data in high-dimensional spaces, manipulate matrices and vectors, and understand the geometric properties of data. It provides the language and framework for expressing and solving problems involving large datasets and complex relationships, albeit within a realm where everything is linear. Calculus, on the other hand, introduces us to the dynamics of change and curves, expanding our capabilities beyond linear structures. This allows us to optimize convex cost functions with gradients, a key element in the training of Machine Learning models.

Building on this foundation, probability and statistics introduce the essential concepts of uncertainty and inference. In Machine Learning, we rarely have access to complete or perfect data. Instead, we work with samples representing larger populations, and our task is to make educated guesses about the underlying patterns. Real-world data is inherently uncertain, and probability provides a framework for modeling this uncertainty, enabling us to quantify the likelihood of different outcomes and make predictions that account for variability.

Though the storytelling makes the content light and engaging, the book never loses sight of the mathematical rigor needed to master these topics.

In this book, you’ll discover:

Intriguing Heist Narratives: Learn key concepts such as hypothesis testing, confidence intervals, and Bayesian reasoning, all embedded in the narrative of one of history's most notorious heists.
Advanced AI Techniques: Dive into Monte Carlo methods, Markov Chains, Gibbs sampling, the Metropolis-Hastings algorithm, and hierarchical Bayesian models—all tied back to the clever strategies of the heist.
Hands-On Learning: Understand the real-world application of statistical methods with accompanying code, designed to solidify each concept through practical exploration.

Join the journey where a diamond heist helps you crack the code of probability and statistics in AI.






ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШЕМ СЮДА!