In the Guide, you'll learn how to: -Use the Ubuntu command line. -Manage users, groups, and file permissions. -Install software on a Ubuntu system, both from the command line and the GUI. -Configure network settings. -Use the vi editor to edit system configuration files. -Install and configure a Samba server for file sharing. -Install SSH for remote system control using public key/private key encryption. -Install a DHCP server for IP address management. -Install a LAMP server. -Install web applications like WordPress and Drupal. -Configure an FTP server. -Manage ebooks. -Convert digital media. -Manage and configure Unity, the default Ubuntu environment. -Manage and halt processes from the command line. -Set up both a VNC server and a client. -Enjoy games on Ubuntu. -And many other topics.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    This book contains my answer to that question. The purpose of the book is to tell the beginning student of advanced mathematics the basic set- theoretic facts of life, and to do so with the minimum of philosophical discourse and logical formalism. The point of view throughout is that of a prospective mathematician anxious to study groups, or integrals, or manifolds. From this point of view the concepts and methods of this book are merely some of the standard mathematical tools; the expert specialist will find nothing new here. Scholarly bibliographical credits and references are out of place in a purely expository book such as this one. The student who gets interested in set theory for its own sake should know, however, that there is much more to the subject than there is in this book. One of the most beautiful sources of set-theoretic wisdom is still Hausdorff's Set theory. A recent and highly readable addition to the literature, with an extensive and up-to-date bibliography, is Axiomatic set theory by Suppes.

     Suzanne and Gertrude is a tale of intermittent griefs and wonderments. How do we live, not just with each other, but with memories, with impermanence, with the inevitable melancholy of being?   Suzanne and Gertrude is a spare novel with a profound impact.

    Winning the 2008 national championship was certainly the highlight, but the most dominant era in school history also includes a national-best 300 wins, three Final Fours and nine Big 12 titles since 2001.The consistency was unmatched.As a sportswriter covering the Jayhawks, first for The Kansas City Star and then for Yahoo! Sports, Jason King was there to chronicle it all. From Roy Williams' stunning departure to Mario's Miracle against Memphis to Kansas' 69-game winning streak at Allen Fieldhouse, King witnessed all the highlights - and lowlights - from 2000 and beyond. In short, he was the ultimate insider.Now you will be, too.With "Beyond the Phog," King provides Kansas fans with an unprecedented glimpse into one of the most memorable eras in the program's rich history. Extensive interviews with nearly 40 players from the last decade, as well as both head coaches, reveal fascinating details about the inner-workings of a true college basketball dynasty.You'll laugh, you'll cry, you'll be riveted - and, at times, shocked. Whatever the case, even the most ardent Kansas supporters will be exposed to candid, behind the scenes stories and anecdotes that, until now, had been confined to the Jayhawks' locker room.Here's a sample of what's inside:• Did Drew Gooden's shoes cost Kansas the 2002 NCAA title?• Nick Collison and Kirk Hinrich lament their final game against Syracuse• Roy Williams provides details about his final few weeks at Kansas and his relationship with Al Bohl• Why did Wayne Simien almost quit basketball?• Jeff Graves comes clean about violating a sacred locker room rule• Russell Robinson describes why he tried to fight his own coach• J.R. Giddens gives his version of the Moon Bar stabbing• Darrell Arthur explains why he's been hesitant to return to campus since winning the 2008 title• Mario Chalmers provides a step-by-step account of his heroic shot against Memphis• Tyshawn Taylor discusses the aftermath of the Jayhawks' 2011 loss to VCU• Josh Selby talks about his decision to enter the NBA draft• And hundreds of other stories from favorites such as Sherron Collins, Keith Langford, Jeff Boschee, Aaron Miles, Michael Lee, Eric Chenowith, Xavier Henry, Luke Axtell, Sasha Kaun, Tyrel Reed, Jeff Hawkins, Brady Morningstar, Darnell Jackson and others.Time has clearly loosened lips in Lawrence. "Beyond the Phog" is an honest, candid look at what really happened during a magical - and often controversial - period in Kansas basketball history.

    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.