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An Introduction to Machine Learning
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of \"boosting,\" how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Book
Business analytics using R - A practical approach
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will:? Write R programs to handle data? Build analytical models and draw useful inferences from them? Discover the basic concepts of data mining and machine learning? Carry out predictive modeling? Define a business issue as an analytical problem.
Book
Explaining prediction models and individual predictions with feature contributions
We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model.
Journal Article
Taming the imperial imagination : colonial knowledge, international relations, and the Anglo-Afghan encounter, 1808-1878
\"Taming the Imperial Imagination marks a novel intervention into the debate on empire and international relations, and offers a new perspective on nineteenth-century Anglo-Afghan relations. Martin J. Bayly shows how, throughout the nineteenth century, the British Empire in India sought to understand and control its peripheries through the use of colonial knowledge. Addressing the fundamental question of what Afghanistan itself meant to the British at the time, he draws on extensive archival research to show how knowledge of Afghanistan was built, refined and warped by an evolving colonial state. This knowledge informed policy choices and cast Afghanistan in a separate legal and normative universe. Beginning with the disorganised exploits of nineteenth-century explorers and ending with the cold strategic logic of the militarised 'scientific frontier', this book tracks the nineteenth-century origins of contemporary policy 'expertise' and the forms of knowledge that inform interventions in Iraq, Afghanistan and elsewhere today. The book develops in three parts, each of which corresponds to a theme. Part one on 'knowledge' examines how British colonial knowledge of Afghanistan was constructed through the experience of early British explorers and their published travel accounts, focusing in particular on the works of Mountstuart Elphinstone, Alexander Burnes, and Charles Masson. Part two on 'policy' looks at how key policy decisions leading to the First Anglo-Afghan War were shaped by the knowledge provided by an Afghanistan 'knowledge community' based on this earlier body of work and the interpretations made by colonial officials. Part three on 'exception' considers the impact of the First Anglo-Afghan War on diplomatic relations, and charts the emergence of a particular 'idea' of Afghanistan mediated by inter-imperial visions of order, and the intellectual and cultural influences of a particular British frontier mentality\"-- Provided by publisher.
Book
Eight years of AutoML: categorisation, review and trends
Knowledge extraction through machine learning techniques has been successfully applied in a large number of application domains. However, apart from the required technical knowledge and background in the application domain, it usually involves a number of time-consuming and repetitive steps. Automated machine learning (AutoML) emerged in 2014 as an attempt to mitigate these issues, making machine learning methods more practicable to both data scientists and domain experts. AutoML is a broad area encompassing a wide range of approaches aimed at addressing a diversity of tasks over the different phases of the knowledge discovery process being automated with specific techniques. To provide a big picture of the whole area, we have conducted a systematic literature review based on a proposed taxonomy that permits categorising 447 primary studies selected from a search of 31,048 papers. This review performs an extensive and rigorous analysis of the AutoML field, scrutinising how the primary studies have addressed the dimensions of the taxonomy, and identifying any gaps that remain unexplored as well as potential future trends. The analysis of these studies has yielded some intriguing findings. For instance, we have observed a significant growth in the number of publications since 2018. Additionally, it is noteworthy that the algorithm selection problem has gradually been superseded by the challenge of workflow composition, which automates more than one phase of the knowledge discovery process simultaneously. Of all the tasks in AutoML, the growth of neural architecture search is particularly noticeable.
Journal Article
SPEck: mining statistically-significant sequential patterns efficiently with exact sampling
We study the problem of efficiently mining statistically-significant sequential patterns from large datasets, under different null models. We consider one null model presented in the literature, and introduce two new ones that preserve different properties of the observed dataset. We describe SPEck, a generic framework for significant sequential pattern mining, that can be instantiated with any null model, when given a procedure for sampling datasets according to the null distribution. For the previously-proposed model, we introduce a novel procedure that samples exactly according to the null distribution, while existing procedures are approximate samplers. Our exact sampler is also more computationally efficient and much faster in practice. For the null models we introduce, we give exact and/or almost uniform samplers. Our experimental evaluation shows how exact samplers can be orders of magnitude faster than approximate ones, and scale well.
Journal Article
Mastering Data Engineering and Analytics with Databricks
In today's data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow--skills critical for today's data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization's data strategy. By the end, you'll not just understand Databricks--you'll command it, positioning yourself as a leader in the data engineering space.
eBook
Evolution of Translational Omics
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
eBook