Sandra Rendgen

Julius Wiedemann

Paolo Ciuccarelli

Richard Saul Wurman

Simon Rogers

A classic, prize-winning novel about an epic migration and a lone woman haunted by the past in frontier Waipu. In the 1850s, a group of settlers established a community at Waipu in the northern part of New Zealand. They were led there by a stern preacher, Norman McLeod. The community had followed him from Scotland in 1817 to found a settlement in Nova Scotia, then subsequently to New Zealand via Australia. Their incredible journeys actually happened, and in this winner of the New Zealand Book Awards, Fiona Kidman breathes life and contemporary relevance into the facts by creating a remarkable fictional story of three women entangled in the migrations - Isabella, her daughter Annie and granddaughter Maria. McLeod's harsh leadership meant that anyone who ran counter to him had to live a life of secrets. The 'secrets' encapsulated the spirit of these women in their varied reactions to McLeod's strict edicts and connect the past to the present and future.

Information Graphics

What is the true human cost of the war in Afghanistan? What are the real effects of the austerity measure? And how did the London riots spread so quickly?Facts are Sacred, the Guardian's award-winning datablog, publishes and analyses seemingly benign data - released under the auspices of transparency - to bring its readers astonishing revelations about the way we live now. It reveals how data has changed our world and what we can learn from it. Now, the most telling findings from the blog are brought together to give us the facts and figures behind the headlines, beautifully illustrated with extensive data visualisations. Ground-breaking and fascinating, it celebrates a resource that has pushed the boundaries of modern journalism and is a manifesto for a new way of seeing things.

The Power of Data

Facts are Sacred

Ever wondered what goes through other people’s minds—their silly questions, their inner anxieties, hopes, and dreams? In What We Ask Google, Simon Rogers explores insights from the world’s biggest dataset: an epic snapshot, two decades long and counting, of our collective brain. What it reveals about us might surprise you. Every June, for instance, the world sees a spike in searches for “How to help a bee.” Reassuringly, people consistently want to know, “Where to donate blood?” after natural disasters. And despite superficial differences (such as the deeply divided world map of cat people vs. dog people), humanity has a lot more in common than we often acknowledge. After all, everywhere around the world, it’s two a.m. when parents want to know how to get their baby to sleep. Brimming with insights that vary from the playful to the profound, What We Ask Google delves into the momentous and the mundane secrets of what we ask when we get the chance to ask anything, offering a surprisingly hopeful picture of humankind.

A Surprisingly Hopeful History of Humankind

What We Ask Google

Jennifer Daniel

Provides an easy-reference, graphically illustrated introduction to space.

Information Graphics: Space

Jenny Broom

Human Body and Animal Kingdom show just how interesting and humorous scientific information can be. Complex facts about the animal kingdom and human body are reinterpreted as stylish infographics which astonish, amuse and inform by turn.

Infographics: Animal Kingdom

Nicholas Blechman

Provides easy-reference, graphically illustrated introductions to complex facts about the animal kingdom.

Information Graphics: Animal Kingdom

Peter Grundy

A miscellany of incredible information visualised by the extraordinary Peter Grundy . What is inside your body? How does it work? And what can it do? What is it that makes you... you?! Seeing is believing with the book that shows you the facts.

Human Body

Mark Girolami

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." —Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." —David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." —Guangzhi Qu, Oakland University, Rochester, Michigan, USA

Simon Rogers

Books

Your next favorite book is out there