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SOURCE: Witten I. Data Mining. Practical Machine Learning Tools and Techniques 5ed 2025
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MEDIAINFO
Textbook in PDF format Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in Machine Learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and Machine Learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Machine Learning provides the technical basis of data mining. It is used to extract information from the raw data in databases—information ideally expressed in a comprehensible form and that can be used for a variety of purposes. The process is one of abstraction, taking the data, warts and all, and inferring whatever structure underlies it. This book is about the tools and techniques of Machine Learning that are used in practical data mining for finding and, if possible describing, structural patterns in data. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent Deep Learning content on topics such as Generative AI (GANs, VAEs, diffusion models), large language models (Transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible Artificial Intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research. - Provides a thorough grounding in Machine Learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Features in-depth information on Deep Learning and probabilistic models - Covers performance improvement techniques, including input preprocessing and combining output from different methods - Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software - Includes all-new exercises for each chapter The book is divided into two parts. Part I is an introduction to machine learning for data mining. The reader will learn the basic ideas, the topmost level, by reading the first three chapters. Chapter 1, “What’s it all about?,” describes, through examples, what machine learning is and where it can be used; it also provides actual practical applications. Chapter 2, “Input: concepts, instances, attributes,” and Chapter 3, “Output: knowledge representation,” cover the different kinds of input and output—or knowledge representation—that are involved. Different kinds of output dictate different styles of algorithm, and Chapter 4, “Algorithms: the basic methods,” describes the basic methods of machine learning, simplified to make them easy to comprehend. Here the principles involved are conveyed in a variety of algorithms without getting involved in intricate details or tricky implementation issues. To make progress in the application of machine learning techniques to particular data mining problems it is essential to be able to measure how well you are doing. Chapter 5, “Credibility: evaluating what’s been learned,” which can be read out of sequence, equips the reader to evaluate the results that are obtained from machine learning, addressing the sometimes complex issues involved in performance evaluation. Chapter 6, “Preparation: data preprocessing and exploratory data analysis,” describes practical topics involved with engineering the input and output to machine learning. In Chapter 7, “Ethics: what are the impacts of what’s been learned?,” we pause to reflect on how data mining technologies can affect the lives of real people in ways that are not always beneficial, and we describe responsible AI methods for ensuring that harmful impacts are minimized. Part II introduces advanced techniques of machine learning for data mining. Chapter 8, “Ensemble learning,” covers techniques that combine the output from different machine learning models and are in many cases a straightforward approach to improving predictive performance. At the lowest and most detailed level, Chapter 9, “Extending instance-based and linear models,” exposes in naked detail the nitty-gritty issues of implementing a spectrum of machine learning algorithms, including the complexities that are necessary for them to work well in practice (but omitting the heavy mathematical machinery that is required for a few of the algorithms). This chapter further covers intermediate topics on turning linear models into powerful classifiers via kernel methods such as support vector machines and multilayer perceptrons, laying the foundation for our treatment of deep learning. Chapter 10, “Deep learning: fundamentals,” explains the key ideas behind deep learning methods including architectures, principles, learning algorithms, and a “bag of tricks” that are essential knowledge for deep learning practitioners. Chapter 11, “Advanced deep learning methods,” describes advanced and recent deep learning methodologies, including generative deep learning, models for natural language processing, and topics that practitioners should be aware of including adversarial examples and knowledge distillation. Chapter 12, “Beyond supervised and unsupervised learning,” looks at semisupervised and multi-instance learning, that is, learning tasks that do not fit neatly in the usual paradigms. Chapter 13, “Probabilistic methods: fundamentals,” and Chapter 14, “Advanced probabilistic methods,” provide a rigorous account of probabilistic methods for machine learning, with the former chapter covering the basics and the latter chapter going into depth and tackling the technical details of these methods, respectively. Chapter 15, “Moving on: applications and their consequences,” discusses the application of data mining methods to areas including text, internet data, images, and speech. With the increasing deployment of these technologies this chapter surfaces the concerns that arise from their use in certain applications such as deepfakes, self-driving cars, and lethal autonomous weapons systems and discusses their impacts on our society as a whole. The book describes most methods used in practical machine learning. Preface Part I: Introduction To Data Mining What’s it all about? Input: concepts, instances, attributes Output: knowledge representation Algorithms: the basic methods Credibility: evaluating what’s been learned Preparation: data preprocessing and exploratory data analysis Ethics: what are the impacts of what’s been learned? Part II. More advanced machine learning schemes Ensemble learning Extending instance-based and linear models Deep learning: fundamentals Advanced deep learning methods Beyond supervised and unsupervised learning Probabilistic methods: fundamentals Advanced probabilistic methods Moving on: applications and their consequences Appendix A. Theoretical foundations Appendix B. The WEKA workbench Appendix C. Implementation details of trees and rules Appendix D. Technical details of deep learning
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