,Table of Contents
PREFACE
INTRODUCTION TO MATHEMATICS IN AI
ESSENTIAL MATHEMATICAL CONCEPTS
STATISTICS FOR AI
OPTIMIZATION IN AI
LINEAR ALGEBRA IN AI
CALCULUS FOR MACHINE LEARNING
PROBABILITY THEORY IN AI
ADVANCED TOPICS IN MATHEMATICS FOR AI
MATHEMATICAL FOUNDATIONS OF NEURAL NETWORKS
MATHEMATICS BEHIND POPULAR MACHINE LEARNING
ALGORITHMS
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
K-Means Clustering
Principal Component Analysis (PCA)
Neural Networks
Gradient Boosting
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Gradient Descent
IMPLEMENTING AI MATHEMATICS CONCEPTS WITH PYTHON
Linear Regression Implementation
Logistic Regression Implementation
Decision Trees Implementation
Random Forests Implementation
Support Vector Machines (SVM) Implementation
Neural Networks Implementation
K-Means Clustering Implementation
Principal Component Analysis (PCA) Implementation
, Gradient Descent Implementation
Recurrent Neural Networks (RNN) Implementation
Long Short-Term Memory (LSTM) Implementation
Gradient Boosting Implementation
POPULAR PYTHON PACKAGES FOR IMPLEMENTING AI
MATHEMATICS
NumPy
SciPy
Pandas
SymPy
Matplotlib
Seaborn
Scikit-Learn
Statsmodels
TensorFlow
PyTorch
APPLICATIONS OF MATHEMATICS AND STATISTICS IN AI
MATHEMATICS IN COMPUTER VISION
MATHEMATICS IN NATURAL LANGUAGE PROCESSING
MATHEMATICS IN REINFORCEMENT LEARNING
CONCLUSION: BUILDING A STRONG MATHEMATICAL FOUNDATION
FOR AI
GLOSSARY
APPENDIX
BIBLIOGRAPHY
, Preface
Preface - Maths for AI
As the field of Artificial Intelligence (AI) continues to evolve and expand, it
has become increasingly clear that a strong mathematical foundation is
essential for understanding and working with AI. The goal of this book,
"Maths for AI," is to provide a comprehensive introduction to the
mathematical and statistical concepts that are fundamental to AI.
The book is divided into 14 chapters, each covering a different aspect of
mathematics and statistics in AI. From the basics of linear algebra and
calculus to advanced topics like probability theory and neural networks, this
book covers it all. The chapters are designed to be self-contained, so readers
can jump in at any point and learn what they need to know.
The first chapter, "Introduction to Mathematics in AI," provides an
overview of the role of mathematics in AI and sets the stage for the rest of
the book. The following chapters cover essential mathematical concepts
such as probability, statistics, optimization, and linear algebra, which are
crucial for understanding machine learning algorithms and neural networks.
In addition to these fundamental concepts, the book also covers advanced
topics like calculus, differential equations, and game theory. These subjects
are often overlooked in other AI texts, but they are essential for a deep
understanding of the field.
Throughout the book, we have included practical examples and exercises to
help readers reinforce their understanding of the concepts covered. We have
also provided suggestions for further reading and resources for those who
want to delve deeper into each topic.
In conclusion, "Maths for AI" is an essential resource for anyone interested
in learning the mathematical and statistical foundations of AI. Whether you
are a student looking to build a strong foundation for your studies or a
professional looking to enhance your skills, this book will provide you with
the knowledge and tools you need to succeed in the field of AI.
PREFACE
INTRODUCTION TO MATHEMATICS IN AI
ESSENTIAL MATHEMATICAL CONCEPTS
STATISTICS FOR AI
OPTIMIZATION IN AI
LINEAR ALGEBRA IN AI
CALCULUS FOR MACHINE LEARNING
PROBABILITY THEORY IN AI
ADVANCED TOPICS IN MATHEMATICS FOR AI
MATHEMATICAL FOUNDATIONS OF NEURAL NETWORKS
MATHEMATICS BEHIND POPULAR MACHINE LEARNING
ALGORITHMS
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
K-Means Clustering
Principal Component Analysis (PCA)
Neural Networks
Gradient Boosting
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Gradient Descent
IMPLEMENTING AI MATHEMATICS CONCEPTS WITH PYTHON
Linear Regression Implementation
Logistic Regression Implementation
Decision Trees Implementation
Random Forests Implementation
Support Vector Machines (SVM) Implementation
Neural Networks Implementation
K-Means Clustering Implementation
Principal Component Analysis (PCA) Implementation
, Gradient Descent Implementation
Recurrent Neural Networks (RNN) Implementation
Long Short-Term Memory (LSTM) Implementation
Gradient Boosting Implementation
POPULAR PYTHON PACKAGES FOR IMPLEMENTING AI
MATHEMATICS
NumPy
SciPy
Pandas
SymPy
Matplotlib
Seaborn
Scikit-Learn
Statsmodels
TensorFlow
PyTorch
APPLICATIONS OF MATHEMATICS AND STATISTICS IN AI
MATHEMATICS IN COMPUTER VISION
MATHEMATICS IN NATURAL LANGUAGE PROCESSING
MATHEMATICS IN REINFORCEMENT LEARNING
CONCLUSION: BUILDING A STRONG MATHEMATICAL FOUNDATION
FOR AI
GLOSSARY
APPENDIX
BIBLIOGRAPHY
, Preface
Preface - Maths for AI
As the field of Artificial Intelligence (AI) continues to evolve and expand, it
has become increasingly clear that a strong mathematical foundation is
essential for understanding and working with AI. The goal of this book,
"Maths for AI," is to provide a comprehensive introduction to the
mathematical and statistical concepts that are fundamental to AI.
The book is divided into 14 chapters, each covering a different aspect of
mathematics and statistics in AI. From the basics of linear algebra and
calculus to advanced topics like probability theory and neural networks, this
book covers it all. The chapters are designed to be self-contained, so readers
can jump in at any point and learn what they need to know.
The first chapter, "Introduction to Mathematics in AI," provides an
overview of the role of mathematics in AI and sets the stage for the rest of
the book. The following chapters cover essential mathematical concepts
such as probability, statistics, optimization, and linear algebra, which are
crucial for understanding machine learning algorithms and neural networks.
In addition to these fundamental concepts, the book also covers advanced
topics like calculus, differential equations, and game theory. These subjects
are often overlooked in other AI texts, but they are essential for a deep
understanding of the field.
Throughout the book, we have included practical examples and exercises to
help readers reinforce their understanding of the concepts covered. We have
also provided suggestions for further reading and resources for those who
want to delve deeper into each topic.
In conclusion, "Maths for AI" is an essential resource for anyone interested
in learning the mathematical and statistical foundations of AI. Whether you
are a student looking to build a strong foundation for your studies or a
professional looking to enhance your skills, this book will provide you with
the knowledge and tools you need to succeed in the field of AI.
