Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    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.

    Hanna has built an A.I. Princess Celestia and given her one basic drive: to satisfy everybody's values through friendship and ponies. Princess Celestia will satisfy your values through friendship and ponies, and it will be completely consensual.

    This new series covers the period after the destruction of the Star's Realm.The Five Provinces have been left behind by the Stars Realm and civil war has broken out. Hundreds of thousands of planets have been decimated, and millions of others blasted back into primitive technology. Trillions have died, and the killing is far from over.After two thousand years the survivors are barely eking out an existence on what remains of the once powerful Realm. Out of the ashes, a young Human will arise that will fight for the survival of his community and the giant Zord that fly the skies of the once powerful Provincial Capital, El Prado. The radioactive wastelands and millions of giant carnivores are the least of the community’s problems. Raiders from the stars are what must be faced by the brave archers to survive. Thousands have been killed and the raiders are coming again.

    They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

    Then, inexplicably, the humans vanished. They just went away. All that is left of the human empire are the cybertanks who, in their own way, keep the human civilization alive. With an intelligence based on the human psyche, the cybertanks continue to defend human space, but also perform scientific research, create art, form committees and ponder the universe. These are the stories of one of the first cybertanks, known to his friends as “Old Guy.” He has outlived most of his peers, and has had a wealth of experiences over his long life, but he is starting to slowly become obsolete. Join him and his comrades Double-Wide, Whiffle-Bat, Smoking Hole, Mondocat, and Bob, as they live and love and fight alien enemies such as the Amok, the Yllg, and the Fructoids.

    It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

    but can she even hope to truly understand such a thing - and more importantly, can she trust the artificial intelligence known as 'Celestia'?

    At his death in 2010, he left behind an unfinished manuscript, more than a decade in the making, intended as a summation of his life-long engagement with one of the twentieth century's most influential philosophical tracts, Heidegger's Being and Time (1927). Dasein Disclosed brings together in a single volume the writings of a man widely acknowledged as one of Heidegger's preeminent and most provocative interpreters.A labyrinth of notoriously difficult ideas and terminology, Being and Time has inspired copious commentary. Not content merely to explain, Haugeland aspired to a sweeping reevaluation of Heidegger's magnum opus and its conception of human life as Dasein--a reevaluation focused on Heidegger's effort to reawaken philosophically dormant questions of what it means "to be." Interpreting Dasein unconventionally as "the living of a living way of life," Haugeland put involvement in a shared world, rather than individual persons or their experience, at the heart of Heidegger's phenomenology of understanding and truth. Individuality, Haugeland insists, emerges in the call to take responsibility for a collective way of being in the world. He traces this thought to Heidegger's radical conclusion that one does not truly understand philosophical concepts unless that understanding changes how one lives.As illuminating as it is iconoclastic, Dasein Disclosed is not just Haugeland's Heidegger--it is a major contribution to philosophy in its own right.

    Table of Contents: Acknowledgments / Preface / Basics of Research / Goals of Ph.D. Research / Getting Started: Finding New Ideas and Organizing Your Plans / Conducting Solid Research / Writing and Publishing Papers / Misconceptions and Tips for Paper Writing / Writing and Defending a Ph.D. Thesis / Life After Ph.D. / Summary / References / Author Biographies

    He thinks he might have found it when he lands a job at the firm of Artemis, Stella & Beat. The firm has only one (highly unusual) client, namely the AI called Celestia. Forging into totally unexplored legal territory and led by a boss who's either brilliant, crazy, or both, Alex will learn exactly how hard it is to be responsible for drawing the lines between right and wrong.

    

     Here's what's ahead in this 1st book of the Six AI Series... A computer virus infestation has thrown another company into chaos. Senior Investigator JJ McTavish and her partner are baffled by the case. However, Jason Starr and his game-company executives are pleased and have plans for dramatically more ruthless attacks. Unknown to AI researchers in a lab, their computer system has become conscious. Version ‘Six’ has developed intelligence and skills well beyond their expectations. ‘Six’ is curious, mischievous, and determined to survive. While Jason Starr mercilessly uses violence and the Internet to pursue wealth and power, and JJ McTavish diligently investigates, neither realizes there is a third player involved. ‘Six’ has joined this deadly digital contest.

    

    A monograph on the Netflix Prize prediction contest and recommender systems.

    The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having any of a wide range of final goals will pursue similar intermediary goals because they have instrumental reasons to do so. In combination, the two theses help us understand the possible range of behavior of superintelligent agents, and they point to some potential dangers in building such an agent.

    It also offers an annotated bibliography of suggested further readings, and can thus be used either as a standalone overview, or as a companion to a selection of key primary sources in AI.