Fans pick 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 12,000+ 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.

Why am I passionate about this?

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.

Why am I passionate about this?

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.

Why am I passionate about this?

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.

Why am I passionate about this?

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.

Why am I passionate about this?

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.

Why am I passionate about this?

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 Condorcet: Political Writings

Per Molander Author Of The Anatomy Of Inequality: Its Social and Economic Origins - and Solutions

From my list on (in)equality and why it is a problem.

Why am I passionate about this?

I was trained in physics and applied mathematics, but my mother—a teacher of literature and history—secured a place for the humanities in my intellectual luggage, and I finally ended up in the social sciences. One of my first encounters with economics was John Nash’s theory of bargaining, illustrating how a wealthy person will gain more from a negotiation than a pauper, thus reinforcing inequality and leading to instability. Decades later, I returned to this problem and found that relatively little had still been done to analyze it. I believe that a combination of mathematical tools and illustrations from history, literature, and philosophy is an appropriate way of approaching the complex of inequality. 

Per's book list on (in)equality and why it is a problem

Per Molander Why did Per love this book?

Discover Condorcet!

Most people, when asked to name a philosopher who wrote about inequality, would think of Rousseau. Condorcet was the last of the Encyclopédistes, young enough to experience the revolution in 1789—sadly, also one of its victims.

Unlike his philosopher colleagues, he participated actively in public policymaking, first in the Ministry of Finance, later as an elected member of the Legislative Assembly after the revolution. He chaired an organization working for the abolition of slavery. He argued for equal rights for women before Olympe de Gouges and Mary Wollstonecraft had published their more well-known pamphlets. He co-authored the Declaration of the Rights of Man and of the Citizen and also wrote a proposal for new constitution for France.

Most importantly, he realized the fundamental role of education as a means to reduce inequality and liberate mankind, and he even developed curricula for the various stages of a general…

By Steven Lukes (editor), Nadia Urbinati (editor),

Why should I read it?

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

What is this book about?

A premium flagship range from Letts Educational, the brand leader in home study. The Premier series is specifically designed to be the most accessible and fresh series on the home study market and to work closely alongside the primary curriculum. The series strengthens numeracy, literacy and ICT skills from playschool right through to secondary school. Each book covers thirty topics to provide thorough revision and a solid learning foundation, and comes with twenty flashcards to give additional visual stimulus for key concepts.


Book cover of Mathematics in Ancient Iraq: A Social History

Amanda H. Podany Author Of Weavers, Scribes, and Kings: A New History of the Ancient Near East

From my list on the lives of real people in ancient Mesopotamia.

Why am I passionate about this?

I’m a historian and professor of ancient Mesopotamia. I was born in the UK but have lived in the US for decades, and from childhood I loved ancient history and archaeology (even through a five-year stint as a bass player before and during college). No matter how long the human race exists in future, we have only one shared ancient global past, the remains of which represent a completely non-renewable resource and source of inspiration. There is plenty left to discover, with much evidence already excavated and awaiting interpretation. It’s a joy to analyze and share the words and life-stories of Mesopotamians in my books—in a conversation that stretches across millennia.

Amanda's book list on the lives of real people in ancient Mesopotamia

Amanda H. Podany Why did Amanda love this book?

Mesopotamian mathematics is a fascinating subject; their numerical system was based on 60, and the ancient thinkers were adept at many types of calculations and word problems. Hundreds of clay tablets reflect their advanced understanding of mathematical principles. Eleanor Robson explains clearly in this book how historians and mathematicians have interpreted the evidence, and she discusses not just specific mathematical texts, how they are understood, and the way ideas were expressed, but she also introduces the scribes who developed and learned it all, and even the buildings in which they worked. The book is a “social history,” as the subtitle notes, and also an intellectual adventure.

By Eleanor Robson,

Why should I read it?

1 author picked Mathematics in Ancient Iraq as one of their favorite books, and they share why you should read it.

What is this book about?

This monumental book traces the origins and development of mathematics in the ancient Middle East, from its earliest beginnings in the fourth millennium BCE to the end of indigenous intellectual culture in the second century BCE when cuneiform writing was gradually abandoned. Eleanor Robson offers a history like no other, examining ancient mathematics within its broader social, political, economic, and religious contexts, and showing that mathematics was not just an abstract discipline for elites but a key component in ordering society and understanding the world. The region of modern-day Iraq is uniquely rich in evidence for ancient mathematics because its…


Book cover of How to Think Like a Mathematician

Lara Alcock Author Of How to Study as a Mathematics Major

From my list on studying undergraduate mathematics.

Why am I passionate about this?

I am a Reader in the Mathematics Education Centre at Loughborough University in the UK. I have always loved mathematics and, when I became a PhD student and started teaching, I realized that how people think about mathematics is fascinating too. I am particularly interested in demystifying the transition to proof-based undergraduate mathematics. I believe that much of effective learning is not about inherent genius but about understanding how theoretical mathematics works and what research tells us about good study strategies. That is what these books, collectively, are about.

Lara's book list on studying undergraduate mathematics

Lara Alcock Why did Lara love this book?

Many undergraduate mathematics books – even those aimed at new students – are dense, technical, and difficult to read at any sort of speed. This is a natural feature of books in a deductive science, but it can be very discouraging, even for dedicated students. Houston’s book covers many ideas useful at the transition to proof-based mathematics, and he has worked extensively and attentively with students at that stage. Consequently, his book maintains high mathematical integrity and has lots of useful exercises while also being an unusually friendly read.

By Kevin Houston,

Why should I read it?

1 author picked How to Think Like a Mathematician as one of their favorite books, and they share why you should read it.

What is this book about?

Looking for a head start in your undergraduate degree in mathematics? Maybe you've already started your degree and feel bewildered by the subject you previously loved? Don't panic! This friendly companion will ease your transition to real mathematical thinking. Working through the book you will develop an arsenal of techniques to help you unlock the meaning of definitions, theorems and proofs, solve problems, and write mathematics effectively. All the major methods of proof - direct method, cases, induction, contradiction and contrapositive - are featured. Concrete examples are used throughout, and you'll get plenty of practice on topics common to many…


Book cover of Mathematical Writing

Lara Alcock Author Of How to Study as a Mathematics Major

From my list on studying undergraduate mathematics.

Why am I passionate about this?

I am a Reader in the Mathematics Education Centre at Loughborough University in the UK. I have always loved mathematics and, when I became a PhD student and started teaching, I realized that how people think about mathematics is fascinating too. I am particularly interested in demystifying the transition to proof-based undergraduate mathematics. I believe that much of effective learning is not about inherent genius but about understanding how theoretical mathematics works and what research tells us about good study strategies. That is what these books, collectively, are about.

Lara's book list on studying undergraduate mathematics

Lara Alcock Why did Lara love this book?

Mathematics requires accurate calculation, and students sometimes think that getting the right answer is enough. But mathematics is also about valid logical arguments, and the demand for clear communication increases through an undergraduate degree. Students, therefore, need to learn to write professionally, with attention to general issues like good grammar, and mathematics-specific issues like accuracy in notation, precision in logical language, and structure in extended arguments. Vivaldi’s book has a great many examples and exercises, and students could benefit from studying it systematically or from dipping into it occasionally and reflecting on small ways to improve.

By Franco Vivaldi,

Why should I read it?

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

What is this book about?

This book teaches the art of writing mathematics, an essential -and difficult- skill for any mathematics student.

The book begins with an informal introduction on basic writing principles and a review of the essential dictionary for mathematics. Writing techniques are developed gradually, from the small to the large: words, phrases, sentences, paragraphs, to end with short compositions. These may represent the introduction of a concept, the abstract of a presentation or the proof of a theorem. Along the way the student will learn how to establish a coherent notation, mix words and symbols effectively, write neat formulae, and structure a…


Book cover of The Mathematical Theory of Communication
Book cover of The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Book cover of Modern Mathematical Statistics with Applications

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