Best Machine Learning Mathematics books

Mathematics for Machine Learning – First Edition

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics.

This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts.

For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials.

Mathematics for Machine Technology – 8th Edition

Strengthen mathematical skills and gain practice using those skills in preparation for success in machine trades or manufacturing with Peterson/Smith’s MATHEMATICS FOR MACHINE TECHNOLOGY, 8E. This comprehensive book connects math concepts to relevant machine applications, using industry-specific examples, realistic illustrations and actual machine functions.

Step-by-step problems and examples progress from general math to more complex trigonometry and solid geometry while demonstrating how math applies to machine trades and manufacturing fields. The authors highlight calculator operations when appropriate.

New coverage in this edition emphasizes spreadsheets and introductory G- and M- codes for CNC programming. Master the practical, vocational and technical applications of math concepts necessary to excel in today’s machine, tool-and-die and tool design industries with this proven book.

Machine Learning: An Applied Mathematics Introduction

A fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the most important techniques.

Chapter list:

1. Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
2. General Matters (In one chapter all of the mathematical concepts you’ll need to know. From jargon and notation to maximum likelihood, from information theory and entropy to bias and variance, from cost functions to confusion matrices, and more)
3. K Nearest Neighbours
4. K Means Clustering
5. Naïve Bayes Classifier
6. Regression Methods
7. Support Vector Machines
8. Self-Organizing Maps
9. Decision Trees
10. Neural Networks
11. Reinforcement Learning

This book includes many real-world examples from a variety of fields including

• finance (volatility modelling)
• economics (interest rates, inflation and GDP)
• politics (classifying politicians according to their voting records)
• business (using CEO speeches to determine stock price movement)
• biology (recognising flower varieties, and using heights and weights of adults to determine gender)
• sociology (classifying locations according to crime statistics)
• gambling (fruit machines and Blackjack)
• marketing (classifying the members of his own website to see who will subscribe to his magazine)

Before Machine Learning Volume 2 – Calculus for A.I: The fundamental mathematics for Data Science and Artificial Intelligence

Does the complexity of calculus in machine learning leave you feeling lost in a thicket of equations? Are you eager to find a guide that maps out this territory with clarity and ease? Enter a unique exploration where the world of calculus is demystified through the fascinating biology of bees, offering a perspective on mathematics that is as enlightening as it is unexpected.

In this book, You will start on a journey through the mathematical landscapes of derivatives, gradients, and algorithms, illuminated by the natural wisdom of bees. Drawing parallels between the meticulous behaviors of these remarkable insects and the principles of calculus, I present a narrative that is rich with insight and alive with humor.

This is not a mere textbook—it’s a dialogue. It’s a story told through the lens of bee biology, where every concept from gradient descent to neural networks is related back to the intuitive understanding of nature’s own algorithms. Together we will find the connections between the disciplined dance of bees and the structured world of mathematics.

Here’s what to expect:

• A fresh take on calculus, viewing complex concepts through the simplicity and order of bee behavior.
• An engaging journey from the basics of calculus to its application in machine learning algorithms like RMSprop, Momentum, and ADAM.
• Key algorithms like linear regression and neural networks.
• Coding exercises and practical tasks that mirror the principles you’ll learn.

Before Machine Learning Volume 1 – Linear Algebra: The fundamental mathematics for Data Science and Artificial Intelligence

Has the abstract nature of linear algebra ever left you overwhelmed? Do you yearn to unlock the essence of machine learning but are bogged down by the intricacy of the mathematics? Dive into a realm where linear algebra unfolds not just as numerical operations, but as a powerful story. A story intertwined with the magic of machine learning, making sense of data, and unraveling algorithms that power tomorrow.

I am Jorge, a mathematician with over a decade of hands-on experience in data science and machine learning. Having navigated the intricate pathways of mathematical computations and machine learning algorithms myself, I wrote this book that differs itself from a traditional text book. With a conversational style and humour, I will guide through what you’ve been seeking on your journey into the depths of linear algebra.

This book isn’t just about understanding linear algebra—it’s about experiencing it. Dive into real-world applications, and grasp concepts that are foundational to machine learning:

Intuitive Understanding: Approach linear algebra as a story, where vectors and matrices come alive, making complex ideas feel intuitive and relatable.

Comprehensive Coverage: From the basics of vector addition and matrix multiplication to advanced topics like eigen decomposition and principal component analysis, get a 360-degree understanding.

Practical Applications: Discover how linear algebra powers algorithms, aiding in data interpretation and model building.

Key takeaways include:

• Mastering vectors and matrices in real-world scenarios.
• The magic behind eigenvectors, eigenvalues, and their applications.
• Gaining insights into advanced topics like the singular value decomposition.
• And this is just the tip of the iceberg. Dive in to uncover the essence of machine learning through the lens of linear algebra, and let mathematics weave its story.

Ready to embark on this transformative journey? Don’t miss out—let the power of linear algebra unveil the mysteries of machine learning.

24/01/2024 – UPDATES

• Refined definition of vectors spaces.
• New example of the dot product.
• Notation fixes.
• Complementary exercises.

Linear Algebra and Optimization for Machine Learning: A Textbook

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:

1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection.

The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.

2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression.

Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks.

A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning.

Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond (De Gruyter Textbook)

This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics.

There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction.

This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field..

Before Machine Learning Volume 3 – Probability and Statistics for A.I: The fundamental mathematics for Data Science and Artificial Intelligence

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. 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.

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.

Python and Math Essentials for Machine Learning: A Beginner’s Guide

Machine Learning, Data Science, and the use of Artificial Intelligence technologies are growing rapidly in our society. Just a few applications include self-driving cars, personal assistants, product recommendations, robotics, data analysis, and web searching. These applications typically involve self-learning systems that are trained based on large amounts of data and the integration of “intelligence” based on algorithms.
To begin mastering the field of machine learning and AI, students must be fluent in the requisite computer science and mathematics topics, and this book provides a comprehensive introduction to both. Readers completing this book will attain a general working knowledge of the Python programming language and math concepts needed, including Statistics, Linear Algebra, and Calculus, to implement and analyze traditional and advanced Machine Learning algorithms.