Book picks similar to
Algorithmic Game Theory by Noam Nisan
game-theory
computer-science
mathematics
algorithms
Calculus
Gilbert Strang - 1991
The author has a direct style. His book presents detailed and intensive explanations. Many diagrams and key examples are used to aid understanding, as well as the application of calculus to physics and engineering and economics. The text is well organized, and it covers single variable and multivariable calculus in depth. An instructor's manual and student guide are available online at http: //ocw.mit.edu/ans7870/resources/Strang/....
Programming Ruby: The Pragmatic Programmers' Guide
Dave Thomas - 2000
When Ruby first burst onto the scene in the Western world, the Pragmatic Programmers were there with the definitive reference manual, Programming Ruby: The Pragmatic Programmer's Guide.Now in its second edition, author Dave Thomas has expanded the famous Pickaxe book with over 200 pages of new content, covering all the improved language features of Ruby 1.8 and standard library modules. The Pickaxe contains four major sections:An acclaimed tutorial on using Ruby.The definitive reference to the language.Complete documentation on all built-in classes, modules, and methodsComplete descriptions of all 98 standard libraries.If you enjoyed the First Edition, you'll appreciate the expanded content, including enhanced coverage of installation, packaging, documenting Ruby source code, threading and synchronization, and enhancing Ruby's capabilities using C-language extensions. Programming for the World Wide Web is easy in Ruby, with new chapters on XML/RPC, SOAP, distributed Ruby, templating systems, and other web services. There's even a new chapter on unit testing.This is the definitive reference manual for Ruby, including a description of all the standard library modules, a complete reference to all built-in classes and modules (including more than 250 significant changes since the First Edition). Coverage of other features has grown tremendously, including details on how to harness the sophisticated capabilities of irb, so you can dynamically examine and experiment with your running code. Ruby is a wonderfully powerful and useful language, and whenever I'm working with it this book is at my side --Martin Fowler, Chief Scientist, ThoughtWorks
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Erik Brynjolfsson - 2014
Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape.A fundamentally optimistic book, The Second Machine Age alters how we think about issues of technological, societal, and economic progress.
In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation
William J. Cook - 2011
In this book, William Cook takes readers on a mathematical excursion, picking up the salesman's trail in the 1800s when Irish mathematician W. R. Hamilton first defined the problem, and venturing to the furthest limits of today's state-of-the-art attempts to solve it. He also explores its many important applications, from genome sequencing and designing computer processors to arranging music and hunting for planets.In Pursuit of the Traveling Salesman travels to the very threshold of our understanding about the nature of complexity, and challenges you yourself to discover the solution to this captivating mathematical problem.
Machine Learning: A Probabilistic Perspective
Kevin P. Murphy - 2012
Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller - 2009
The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Accelerated C++: Practical Programming by Example
Andrew Koenig - 2000
Based on the authors' intensive summer C++ courses at Stanford University, Accelerated C++ covers virtually every concept that most professional C++ programmers will ever use -- but it turns the traditional C++ curriculum upside down, starting with the high-level C++ data structures and algorithms that let you write robust programs immediately. Once you're getting results, Accelerated C++ takes you under the hood, introducing complex language features such as memory management in context, and explaining exactly how and when to use them. From start to finish, the book concentrates on solving problems, rather than learning language and library features for their own sake. The result: You'll be writing real-world programs in no time -- and outstanding code faster than you ever imagined.
A New Kind of Science
Stephen Wolfram - 1997
Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton
Doing Math with Python
Amit Saha - 2015
Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.
Machine Learning for Hackers
Drew Conway - 2012
Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data
Data Science
John D. Kelleher - 2018
Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
Introduction to Information Retrieval
Christopher D. Manning - 2008
Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
Programming Interviews Exposed: Secrets to Landing Your Next Job (Programmer to Programmer)
John Mongan - 2000
This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want.What you will learn from this bookTips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations.Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 2
Dave Shreiner - 1999
The OpenGL Programming Guide provides definitive and comprehensive information on OpenGL and the OpenGL Utility Library. It is far and away the most important book on OpenGL, and is commonly referred to by programmers simply as the Red book. Last summer the OpenGL Architectural Review Board (ARB) announced the release of the version 2.0 standard, incorporating the OpenGL Shader Language (GLSL) officially into the spec. This is the biggest change in OpenGL since its inception. This new edition will provide basic information about GLSL itself, as well as all the other changes to the 1.5 and 1.0 versions. the official, comprehensive guide to GLSL itself. A few years ago, pundits were predicting the imminent demise of OpenGL. Far from expiring, however, OpenGL has had a resurgence in the last couple years, and has solidified its position as the defacto standard for high-quality computer graphics. This book remains the necessary guide for any developer doing graphics programming. The sample source code in the book will be available on the book's web site.
The Mythical Man-Month: Essays on Software Engineering
Frederick P. Brooks Jr. - 1975
With a blend of software engineering facts and thought-provoking opinions, Fred Brooks offers insight for anyone managing complex projects. These essays draw from his experience as project manager for the IBM System/360 computer family and then for OS/360, its massive software system. Now, 45 years after the initial publication of his book, Brooks has revisited his original ideas and added new thoughts and advice, both for readers already familiar with his work and for readers discovering it for the first time.The added chapters contain (1) a crisp condensation of all the propositions asserted in the original book, including Brooks' central argument in The Mythical Man-Month: that large programming projects suffer management problems different from small ones due to the division of labor; that the conceptual integrity of the product is therefore critical; and that it is difficult but possible to achieve this unity; (2) Brooks' view of these propositions a generation later; (3) a reprint of his classic 1986 paper "No Silver Bullet"; and (4) today's thoughts on the 1986 assertion, "There will be no silver bullet within ten years."