A free e-Magazine written and published by Young Naturalists' based in Chennai.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

First published in 2004. Since The Little Ice Age was published in 1988, interest in climatic history has grown rapidly and research in the area has flourished. A vast amount of new data has become available from sources such as ice cores, speleothems and tree rings. The picture that we have of past climates and glacier oscillations has extended further into the past and has become more detailed. However, the knowledge of climate change on the decennial and centennial timescale, to which glacier history can contribute, is scarce and is in demand when attempting to predict future change, especially with regard to global warming. New chapters and material have been included throughout the book, which tend to confirm and elaborate on the conclusions of the first edition. The glacial evidence has been presented in the context of the oceanographic and icecap studies that have provided such exciting results. Little Ice Ages is structured in three parts: • Part 1 details the evidence for glacier variations in the last thousand years in different parts of the world and the associated climatic fluctuations. • Part 2 brings together the evidence for the timing of glacier variations in the course of the Holocene. • Part 3 views the Holocene record in a longer time context, especially as it appears in ice cores, and goes on to consider the likely causes of climatic variability on a Little Ice Age timescale and some of its physical, biologi□cal and human consequences. It becomes apparent in Little Ice Ages that the glacier record provides a valuable indication of the nature of climatic fluctuations on the land areas of the globe. The record points to periods of cooling which were more numerous and less continuous than was believed to be the case twenty years ago. There appears to be no single explanation for the variability. Volcanism, solar variability and ocean currents have all played their parts and prediction continues to present many problems. Some authorities have thrown doubt on the existence of the Little Ice Age, but Little Ice Ages makes the case for a climatic sequence that can usefully be called the Little Ice Age and which had predecessors occurring at intervals of several centuries throughout much of the last 10,000 years.

This book is an introduction to the language and standard proof methods of mathematics. It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.