100 books like Introduction to Modern Nonparametric Statistics

By James J. Higgins,

Here are 100 books that Introduction to Modern Nonparametric Statistics fans have personally recommended if you like Introduction to Modern Nonparametric Statistics. Shepherd is a community of 9,000+ authors and super readers sharing their favorite books with the world.

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Book cover of The Mathematical Theory of Communication

Chris Conlan Author Of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

From my list on mathematics for quant finance.

Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.

Chris' book list on mathematics for quant finance

Chris Conlan Why did Chris love this book?

While studying computer networks, Claude Shannon did something pretty impressive. He reformulated the majority of classical statistics from scratch using the language and concepts of computer science. 

Statistical noise? There’s a new word for that; it’s called entropy. Also, it turns out it is a good thing, not a bad thing because entropy is equal to the information content or a data set. Tired of minimizing the squared error of everything? That’s fine, minimize the log of its likelihood instead. It does the same thing. This book challenges the assumptions of classical statistics in a way that fits neatly in the mind of a computer scientist. As a quant trader, this book will help you understand and measure the information content of data, which is critical to your success.

By Claude E. Shannon, Warren Weaver,

Why should I read it?

2 authors picked The Mathematical Theory of Communication as one of their favorite books, and they share why you should read it.

What is this book about?

Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication theory more than fifty years ago. Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.


Book cover of The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Chris Conlan Author Of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

From my list on mathematics for quant finance.

Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.

Chris' book list on mathematics for quant finance

Chris Conlan Why did Chris love this book?

This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.

In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

Why should I read it?

2 authors picked The Elements of Statistical Learning as one of their favorite books, and they share why you should read it.

What is this book about?

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major…


Book cover of Modern Mathematical Statistics with Applications

Chris Conlan Author Of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

From my list on mathematics for quant finance.

Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.

Chris' book list on mathematics for quant finance

Chris Conlan Why did Chris love this book?

One of my favorite professors, Gretchen Martinet, used this to teach a course called “Mathematical Statistics” when I was at the University of Virginia. It is an extremely profound course full of dense but fundamental mathematical proofs in classical statistics. 

You will learn why the formula for the normal distribution is the way it is, why the sum of squares appears everywhere in statistics, and how to fit a linear regression by hand. In the same way calculus elevates our understanding of rates of changes, the book elevates your understanding of samples, averages, and distributions. Quant trading requires an intuitive sense of how data, models, and aggregates work, making this content essential for your success.

By Jay L. DeVore, Kenneth N. Berk,

Why should I read it?

1 author picked Modern Mathematical Statistics with Applications as one of their favorite books, and they share why you should read it.

What is this book about?

Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data.

The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The…


Book cover of Probability: The Science of Uncertainty: With Applications to Investments, Insurance, and Engineering

Chris Conlan Author Of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

From my list on mathematics for quant finance.

Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.

Chris' book list on mathematics for quant finance

Chris Conlan Why did Chris love this book?

Everyone knows what probability is, and we all understand how a coin flip works, but not everyone can explain the optimal betting strategies for a roulette table. We don’t study probability to understand the likelihood of events. We study probability to understand the expected outcomes of business processes that depend on those events.

In other words, this book won’t just teach you about probabilities, it will teach you about business strategies associated with those probabilities. It will help you answer a question like: How do I maximize the profit on this life insurance policy, given this set of survival probabilities? It isn’t just a likelihood question, it is a business question. I highly recommend that anyone studying probability does so through an actuarial lens.

By Michael A. Bean,

Why should I read it?

1 author picked Probability as one of their favorite books, and they share why you should read it.

What is this book about?

This book covers the basic probability of distributions with an emphasis on applications from the areas of investments, insurance, and engineering. Written by a Fellow of the Casualty Actuarial Society and the Society of Actuaries with many years of experience as a university professor and industry practitioner, the book is suitable as a text for senior undergraduate and beginning graduate students in mathematics, statistics, actuarial science, finance, or engineering as well as a reference for practitioners in these fields. The book is particularly well suited for students preparing for professional exams, and for several years it has been recommended as…


Book cover of Mathematics for Machine Learning

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

From my list on machine learning for beginners.

Who am I?

I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.  

Yuxi's book list on machine learning for beginners

Yuxi (Hayden) Liu Why did Yuxi love this book?

The book is a well-curated collection of the essential mathematical concepts that form ML. You may experience a cultural shock jumping to this book from the previous one, because the writing in this book is a bit formal. However, it is the missing but necessary piece for building solid foundations for practical ML. You will find it more valuable combining the intuition behind ML that you gained previously. And the explanations in the book are succinct and from the ML perspectives. For instance, partial derivatives are explained in terms of neural network weight optimization. I wish the concepts in Linear Algebra, Vector Calculus, and Probability courses back in college were introduced this way so I understand better how they are applied.  

By Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Why should I read it?

1 author picked Mathematics for Machine Learning as one of their favorite books, and they share why you should read it.

What is this book about?

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…


Book cover of Computer Vision: Models, Learning, and Inference

Mark S. Nixon Author Of Feature Extraction and Image Processing for Computer Vision

From my list on computer vision from a veteran professor.

Who am I?

It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.

Mark's book list on computer vision from a veteran professor

Mark S. Nixon Why did Mark love this book?

This fine book is about learning the relationships between what is seen in an image, and what is known about the world. It’s a counterpart to our book on feature extraction and it shows you what can be achieved with the features. It’s not for those who shy from maths, as is the case for all of the books here. So that you can build the techniques, Simon’s book also includes a wide variety of algorithms to help you on your way.

By Simon J.D. Prince,

Why should I read it?

1 author picked Computer Vision as one of their favorite books, and they share why you should read it.

What is this book about?

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build…


Book cover of The Beginner's Guide to Constructing the Universe: The Mathematical Archetypes of Nature, Art, and Science

Elizabeth E. Botchis Author Of Awakening the Holographic Human: Nature's Path to Healing and Higher Consciousness

From my list on healing ourselves and our planet.

Who am I?

Lilli Botchis, PhD, is a psycho-spiritual counselor, educator, and vibrational medicine developer with four decades of experience in advanced body/soul wellness and the development of higher consciousness. Her expertise includes botanicals, gems, color, flower essences, bio-energy therapies, and holographic soul readings. Lilli is an alchemist, mystic, and translator of Nature’s language as it speaks to our soul. A brilliant researcher in the field of consciousness, she understands the interconnectedness of Nature and the human being and is known as an extraordinary emissary of the natural world. Lilli has been inducted into the Sovereign Order of St. John of Jerusalem, Knights Hospitaller. Many seek her out for her visionary insights and compassionate wisdom.

Elizabeth's book list on healing ourselves and our planet

Elizabeth E. Botchis Why did Elizabeth love this book?

According to Michael Schneider, "The universe may be a mystery, but it's no secret." This book is a comprehensive yet simple visual guide to understanding the hidden meaning in the mathematical composition of all physical form. It is fun and fascinating to discover the sacred geometry visible throughout nature, in flowers, crystals, plants, shells, and the human body. You don't have to be a mathematician to see the beauty and symmetry of these patterns in every expression of God's creation, once revealed.

By Michael S. Schneider,

Why should I read it?

1 author picked The Beginner's Guide to Constructing the Universe as one of their favorite books, and they share why you should read it.

What is this book about?

Discover how mathematical sequences abound in our natural world in this definitive exploration of the geography of the cosmos

You need not be a philosopher or a botanist, and certainly not a mathematician, to enjoy the bounty of the world around us. But is there some sort of order, a pattern, to the things that we see in the sky, on the ground, at the beach? In A Beginner's Guide to Constructing the Universe, Michael Schneider, an education writer and computer consultant, combines science, philosophy, art, and common sense to reaffirm what the ancients observed: that a consistent language of…


Book cover of Alex Through the Looking-Glass

David Acheson Author Of The Wonder Book of Geometry: A Mathematical Story

From my list on mathematics for the general reader.

Who am I?

I am an applied mathematician at Oxford University, and author of the bestseller 1089 and All That, which has now been translated into 13 languages. In 1992 I discovered a strange mathematical theorem – loosely related to the Indian Rope Trick - which eventually featured on BBC television. My books and public lectures are now aimed at bringing mainstream mathematics to the general public in new and exciting ways.

David's book list on mathematics for the general reader

David Acheson Why did David love this book?

This is a sequel to Alex Bellos's bestseller Alex's Adventures in Numberland, but more focused on applications of mathematics to the real world, especially through physics. Many of these were known to me, particularly when they involved calculus, but I greatly enjoyed Alex's distinctive and novel way of putting across sophisticated ideas, in part by interspersing them with personal interviews with mathematicians of all kinds.  

By Alex Bellos,

Why should I read it?

1 author picked Alex Through the Looking-Glass as one of their favorite books, and they share why you should read it.

What is this book about?

From triangles, rotations and power laws, to fractals, cones and curves, bestselling author Alex Bellos takes you on a journey of mathematical discovery with his signature wit, engaging stories and limitless enthusiasm. As he narrates a series of eye-opening encounters with lively personalities all over the world, Alex demonstrates how numbers have come to be our friends, are fascinating and extremely accessible, and how they have changed our world.

He turns even the dreaded calculus into an easy-to-grasp mathematical exposition, and sifts through over 30,000 survey submissions to reveal the world's favourite number. In Germany, he meets the engineer who…


Book cover of Teaching and Learning Algebra

David Acheson Author Of The Wonder Book of Geometry: A Mathematical Story

From my list on mathematics for the general reader.

Who am I?

I am an applied mathematician at Oxford University, and author of the bestseller 1089 and All That, which has now been translated into 13 languages. In 1992 I discovered a strange mathematical theorem – loosely related to the Indian Rope Trick - which eventually featured on BBC television. My books and public lectures are now aimed at bringing mainstream mathematics to the general public in new and exciting ways.

David's book list on mathematics for the general reader

David Acheson Why did David love this book?

This may seem an odd choice, but as a maths popularizer I need to know all that I can about why some people find the main elements of the subject so difficult. I found Doug French's book exceptionally helpful in this respect, even though it is aimed principally at high school teachers. This is partly because he focuses throughout on the most important mathematical ideas and difficulties. Moreover, the scope is wider than the title suggests, for he also ventures imaginatively into both geometry and calculus.

By Doug French,

Why should I read it?

1 author picked Teaching and Learning Algebra as one of their favorite books, and they share why you should read it.

What is this book about?

Continuum has repackaged some of its key academic backlist titles to make them available at a more affordable price. These reissues will have new ISBNs, distinctive jackets and strong branding. They cover a range of subject areas that have a continuing student sale and make great supplementary reading more accessible. A comprehensive, authoritative and constructive guide to teaching algebra.


Book cover of An Introduction to the Mathematics of Financial Derivatives

Alain Ruttiens Author Of Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues

From my list on quantitative finance applied to financial markets.

Who am I?

Having a master's degree in chemical engineering, I wasn't destined to work in the area of quantitative finance… the reason why I professionally moved to this discipline aren't worth exposing, but as a matter of fact, I've been quickly fascinated by this science, and encountered some of my favorites, such as maths and statistics, as used in the traditional activity of an engineer. And I had many opportunities of combining the knowledge and practice of financial markets with pragmatism, typically of the engineer’s education, i.e. oriented toward problem solving. In addition, I've always loved teaching, and writing books on financial markets & instruments, hence the importance I'm giving to pedagogy in professional books.

Alain's book list on quantitative finance applied to financial markets

Alain Ruttiens Why did Alain love this book?

Having read or browsed many books dedicated to the mathematics of options and other derivative instruments, I unquestionably consider Neftci’s book as by far the best choice.

Starting with the fundamentals, it goes much further than a simple “introduction”, and typically fits with the needs of a “quant” specializing in options, with a good balance between pure theoretical, mathematical developments (such as Partial Differential Equations, Girsanov theorem, Markov processes, etc) and practical applications on option pricing. 

By Salih N. Neftci,

Why should I read it?

1 author picked An Introduction to the Mathematics of Financial Derivatives as one of their favorite books, and they share why you should read it.

What is this book about?

An Introduction to the Mathematics of Financial Derivatives, Second Edition, introduces the mathematics underlying the pricing of derivatives.

The increased interest in dynamic pricing models stems from their applicability to practical situations: with the freeing of exchange, interest rates, and capital controls, the market for derivative products has matured and pricing models have become more accurate. This updated edition has six new chapters and chapter-concluding exercises, plus one thoroughly expanded chapter. The text answers the need for a resource targeting professionals, Ph.D. students, and advanced MBA students who are specifically interested in financial derivatives.

This edition is also designed to…


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