Daphne Koller

Daphne Koller is Professor in the Department of Computer Science at Stanford University.

  • Probabilistic Graphical Models

    Probabilistic Graphical Models

    Principles and Techniques

    Daphne Koller and Nir Friedman

    A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

    Most tasks require a person or an automated system to reason—to reach conclusions based on available information. 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.

    • Hardcover $125.00 £102.00


  • Sound Unbound

    Sound Unbound

    Sampling Digital Music and Culture

    Paul D. Miller

    The role of sound and digital media in an information-based society: artists—from Steve Reich and Pierre Boulez to Chuck D and Moby—describe their work.

    If Rhythm Science was about the flow of things, Sound Unbound is about the remix—how music, art, and literature have blurred the lines between what an artist can do and what a composer can create. In Sound Unbound, Rhythm Science author Paul Miller aka DJ Spooky that Subliminal Kid asks artists to describe their work and compositional strategies in their own words. These are reports from the front lines on the role of sound and digital media in an information-based society. The topics are as diverse as the contributors: composer Steve Reich offers a memoir of his life with technology, from tape loops to video opera; Miller himself considers sampling and civilization; novelist Jonathan Lethem writes about appropriation and plagiarism; science fiction writer Bruce Sterling looks at dead media; Ron Eglash examines racial signifiers in electrical engineering; media activist Naeem Mohaiemen explores the influence of Islam on hip hop; rapper Chuck D contributes “Three Pieces”; musician Brian Eno explores the sound and history of bells; Hans Ulrich Obrist and Philippe Parreno interview composer-conductor Pierre Boulez; and much more. “Press 'play,'” Miller writes, “and this anthology says 'here goes.'”

    The groundbreaking music that accompanies the book features Nam Jun Paik, the Dada Movement, John Cage, Sonic Youth, and many other examples of avant-garde music. Most of this content comes from the archives of Sub Rosa, a legendary record label that has been the benchmark for archival sounds since the beginnings of electronic music. To receive these free music files, readers may send an email to the address listed in the book.

    Contributors David Allenby, Pierre Boulez, Catherine Corman, Chuck D, Erik Davis, Scott De Lahunta, Manuel DeLanda, Cory Doctorow, Eveline Domnitch, Frances Dyson, Ron Eglash, Brian Eno, Dmitry Gelfand, Dick Hebdige, Lee Hirsch, Vijay Iyer, Ken Jordan, Douglas Kahn, Daphne Keller, Beryl Korot, Jaron Lanier, Joseph Lanza, Jonathan Lethem, Carlo McCormick, Paul D. Miller aka DJ Spooky that Subliminal Kid, Moby, Naeem Mohaiemen, Alondra Nelson, Keith and Mendi Obadike, Hans Ulrich Obrist, Pauline Oliveros, Philippe Parreno, Ibrahim Quaraishi, Steve Reich, Simon Reynolds, Scanner aka Robin Rimbaud, Nadine Robinson, Daniel Bernard Roumain (DBR), Alex Steinweiss, Bruce Sterling, Lucy Walker, Saul Williams, Jeff E. Winner

    • Paperback $39.95 £32.00
  • Introduction to Statistical Relational Learning

    Introduction to Statistical Relational Learning

    Lise Getoor and Ben Taskar

    Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.

    Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

    • Hardcover $65.00 £55.00
    • Paperback $55.00 £45.00