the Academic NEST

Ben Stephenson ... 219 pages - Publisher: Springer; 2nd edition (July, 2019) ... Language: English - ISBN-10: 3030188728 - ISBN-13: 978-3030188726.

This student-friendly textbook encourages the development of programming skills through active practice by focusing on exercises that support hands-on learning. The Python Workbook provides a compendium of 186 exercises, spanning a variety of academic disciplines and everyday situations. Solutions to selected exercises are also provided, supported by brief annotations that explain the technique used to solve the problem, or highlight a specific point of Python syntax. This enhanced new edition has been thoroughly updated and expanded with additional exercises, along with concise introductions that outline the core concepts needed to solve them. The exercises and solutions require no prior background knowledge, beyond the material covered in a typical introductory Python programming course.

Features: Uses an accessible writing style and easy-to-follow structure; includes a mixture of classic exercises from the fields of computer science and mathematics, along with exercises that connect to other academic disciplines; presents the solutions to approximately half of the exercises; provides annotations alongside the solutions, which explain the approach taken to solve the problem and relevant aspects of Python syntax; offers a variety of exercises of different lengths and difficulties; contains exercises that encourage the development of programming skills using if statements, loops, basic functions, lists, dictionaries, files, and recursive functions. Undergraduate students enrolled in their first programming course and wishing to enhance their programming abilities will find the exercises and solutions provided in this book to be ideal for their needs.

Bertrand S. Clarke, Jennifer L. Clarke ... 656 pages - Publisher: Cambridge Univ. Press; (April, 2018) ... Language: English - ISBN-10: 1107028280 - ISBN-13: 978-1107028289.

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui ... 389 pages - Publisher: Springer; (March, 2019) ... Language: English - ISBN-10: 3030114783 - ISBN-13: 978-303011478.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Ryosuke Kitamura, Kazunari Sako ... 188 pages - Publisher: CRC Press; (August, 2019) ... Language: English - AmazonSIN: B07W86B2PY.

Soil is fundamentally a multi-phase material – consisting of solid particles, water and air. In soil mechanics and geotechnical engineering it is widely treated as an elastic, elastoplastic or visco-elastoplastic material, and consequently regarded as a continuum body. However, this book explores an alternative approach, considering soil as a multi-phase and discrete material and applying basic Newtonian mechanics rather than analytical mechanics. It applies microscopic models to the solid phase and fluid phases, and then introduces probability theory and statistics to derive average physical quantities which correspond to the soil‘s macroscopic physical properties such as void ratio and water content. This book is particularly focused on the mechanical behaviour of dry, partially saturated and full saturated sandy soil, as much of the physicochemical microscopic characteristic of clayey soil is still not clear. It explores the inter-particle forces at the point of contact of soil particles and the resultant inter-particle stresses, instead of the total stress and effective stress which are studied in mainstream soil mechanics. Deformation and strength behaviour, soil-water characteristic curves, and permeability coefficients of water and air are then derived simply from grain size distribution, soil particle density, void ratio and water content. A useful reference for consultants, professional engineers, researchers and public sector organisations involved in unsaturated soil tests. Advanced undergraduate and postgraduate students on Unsaturated Soil Mechanics courses will also find it a valuable text to study.

Ralf T. Kreutzer, Marie Sirrenberg ... 313 pages - Publisher: Springer; (September, 2019) ... Language: English - ISBN-10: 3030252701 - ISBN-13: 978-3030252700.

Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey.

Naiyang Deng, Yingjie Tian, Chunhua Zhang ... 363 pages - Publisher: Chapman and Hall/CRC; (December, 2012) ... Language: English - AmazonSIN: B00OD4GXCI.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

Kieth A. Carlson, Jennifer R. Winquist ... 656 pages - Publisher: SAGE Publications, Inc; 2nd edition (February, 2017) ... Language: English - ISBN-10: 148337873X - ISBN-13: 978-1483378732.

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.

Narayan C. Giri ... 537 pages - Publisher: CRC Press; 2nd edition (January, 2019) ... Language: English - AmazonSIN: B07M7XVF7J.

Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of statistical problems, covering the broad range of statistical inference.; Requiring a prerequisite in calculus for complete understanding of the topics discussed, the Second Edition contains new material on: univariate distributions; multivariate distributions; large-sample methods; decision theory; and applications of ANOVA.; A primary text for a year-long undergraduate course in statistics (but easily adapted for a one-semester course in probability only), Introduction to Probability and Statistics is for undergraduate students in a wide range of disciplines-statistics, probability, mathematics, social science, economics, engineering, agriculture, biometry, and education.

Larry Wasserman ... 442 pages - Publisher: Springer; (September, 2004) ... Language: English - ISBN-10: 0387402721 - ISBN-13: 978-0387402727.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Kuniaki Sato, Yoshiaki Iwasa ... 204 pages - Publisher: Springer; (June, 2011) ... Language: English - AmazonSIN: B000PY48N6.

Ethem Alpaydin ... 640 pages - Publisher: Phi; 3rd edition (2015) ... Language: English - ISBN-10: 8120350782 - ISBN-13: 978-8120350786.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.