Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    Relevant psychological principles will help you predict your viewer's emotional reaction to your photographic images, giving you more power, control, and tools for communicating your desired message. Knowing how our minds work helps photographers, graphic designers, videographers, animators, and visual communicators both create and critique sophisticated works of visual art. Benefit from this insight in your work. Topics covered in this book: gestalt grouping, memory and association, space, time, color, contours, illusion and ambiguity, morphics, personality, subliminals, critiquing photographs, and rhetoric.

    Each chapter introduces a new bad actor in the history of warfare, details his conquests, and features weapons and fortifications to defend against him and his minions. Clear step-by-step instructions, diagrams, and photographs show how to build a dozen projects, including “Da Vinci’s Catapult,” “Carpini’s Crossbow,” a “Crusader-Proof Moat,” “Alexander’s Tortoise,” and the “Cheval-de-frise.” With a strong emphasis on safety, the book also gives tips on troubleshooting, explains the physics behind many of the projects, and shows where to buy the materials. By the time they’ve reached the last page, at-home defenders everywhere will have succeeded in creating a fully fortified home.

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    *Become a Pro in Minecraft!*Extremely Easy to Read*Superior Survival Techniques & Tips*Outstanding Tricks to Help You Master The Game*Explore New Minecraft World You Never Knew About!

    It divides into two parts, each of which provides enough material for a one-semester graduate course. The first part deals chiefly with the isotropic and homogeneous average universe; the second part concentrates on the departures from the average universe. Throughout the book the author presents detailed analytic calculations of cosmological phenomena, rather than just report results obtained elsewhere by numerical computation. The book is up to date, and gives detailed accounts of topics such as recombination, microwave background polarization, leptogenesis, gravitational lensing, structure formation, and multifield inflation, that are usually treated superficially if at all in treatises on cosmology. Copious references to current research literature are supplied. Appendices include a brief introduction to general relativity, and a detailed derivation of the Boltzmann equation for photons and neutrinos used in calculations of cosmological evolution. Also provided is an assortment of problems.

    one in 25 of the people around you is a psychopath, hiding and living a secret life. Psychopaths take what they want, using their charm and wits. They feel only the emotions of a predator. In this book Pieter Hintjens decodes the mystery of the psychopath. Why do such people exist? How do they operate? And most critical of all, can we learn to avoid them, or escape them? The answers will enlighten you. This book delivers practical tools and techniques to survive the most difficult people.

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    For the calculus-based General Physics course primarily taken by engineers and scientists.

    Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points.The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencesNow current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.

    Provides a review of introductory noncalculus-based physics for those who do not have a strong background in mathematics.

    Numerous examples are provided to reinforce the concepts and relevant case studies illustrate the concepts and mechanisms described.

    It features a wealth of new material, including how to make the most of Web casts and remote demonstrations, how to give impressive snap demonstrations, and how to use CRM/SFA systems effectively.

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a detailed list of commands to follow blindly. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are called algorithms. In practice, they want to give computers the ability to Learn and to Adapt. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it. Supervised Learning Algorithms K-Nearest Neighbour Naïve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Neural Networks Decision Trees