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The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep
The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who
This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.The book has been updated with the latest research in massive data,
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a
Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine
Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or
Part I Transparency in Machine Learning.- Part II Visual Explanation of Machine Learning Process.- Part III Algorithmic Explanation of Machine Learning Models.- Part IV User Cognitive Responses in ML-Based Decision Making.- Part V Human and Evaluation of Machine Learning.- Part VI Domain Knowledge
Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new
Gain a clear understanding of the fundamental concepts and applications behind operations and supply chain management with the reader-friendly approach in Collier/Evans' popular OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 2E. The authors present detailed, solved problems throughout this edition to
Discover a comprehensive exploration of recent developments and fundamental concepts in the applications of metasurfaces. In Electromagnetic Metasurfaces: Theory and Applications, distinguished researchers and authors Karim Achouri and Christophe Caloz deliver an introduction to the fundamentals
This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual
In Cloud Ethics Louise Amoore examines how machine learning algorithms are transforming the ethics and politics of contemporary society. Conceptualizing algorithms as ethicopolitical entities that are entangled with the data attributes of people, Amoore outlines how algorithms give incomplete
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature,
Prerequisite Concepts and Notations.- Basic Homotopy.- The Fundamental Groups.-Covering Spaces.- Fibre Bundles, Vector Bundles and K-theory.- Geometry of Simplicial Complexes and Fundamental Groups.- Higher Homotopy Groups.- Products in Higher Homotopy Groups.- CW-complexes and Homotopy.-
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo
'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
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in