A First Course In Statistical Learning

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A First Course In Statistical Learning
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Author : Johannes Lederer
language : en
Publisher: Springer Nature
Release Date : 2025-02-25
A First Course In Statistical Learning written by Johannes Lederer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-25 with Computers categories.
This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
A First Course In Machine Learning
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Author : Simon Rogers
language : en
Publisher: CRC Press
Release Date : 2016-10-14
A First Course In Machine Learning written by Simon Rogers and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-14 with Computers categories.
Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"
A First Course In Machine Learning
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Author : Mark Girolami
language : en
Publisher: CRC Press
Release Date : 2011-10-25
A First Course In Machine Learning written by Mark Girolami and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-25 with Business & Economics categories.
A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
Machine Learning
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Author : Stephen Marsland
language : en
Publisher: CRC Press
Release Date : 2015-09-15
Machine Learning written by Stephen Marsland and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-15 with Computers categories.
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
Cost Sensitive Machine Learning
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Author : Balaji Krishnapuram
language : en
Publisher: CRC Press
Release Date : 2011-12-19
Cost Sensitive Machine Learning written by Balaji Krishnapuram and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-19 with Computers categories.
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
Computational Trust Models And Machine Learning
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Author : Xin Liu
language : en
Publisher: CRC Press
Release Date : 2014-10-29
Computational Trust Models And Machine Learning written by Xin Liu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-29 with Computers categories.
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
Introduction To Machine Learning With Applications In Information Security
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Author : Mark Stamp
language : en
Publisher: CRC Press
Release Date : 2022-09-27
Introduction To Machine Learning With Applications In Information Security written by Mark Stamp and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-27 with Business & Economics categories.
Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec. Finally, several cutting-edge deep learning topics are discussed, including dropout regularization, attention, explainability, and adversarial attacks. Most of the examples in the book are drawn from the field of information security, with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming, and elementary computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/.
Next Generation Wireless Networks Meet Advanced Machine Learning Applications
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Author : Comşa, Ioan-Sorin
language : en
Publisher: IGI Global
Release Date : 2019-01-25
Next Generation Wireless Networks Meet Advanced Machine Learning Applications written by Comşa, Ioan-Sorin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-25 with Technology & Engineering categories.
The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
Machine Learning Animated
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Author : Mark Liu
language : en
Publisher: CRC Press
Release Date : 2023-10-31
Machine Learning Animated written by Mark Liu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-31 with Computers categories.
The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider. Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics. Access the book's repository at: https://github.com/markhliu/MLA
Statistical Reinforcement Learning
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Author : Masashi Sugiyama
language : en
Publisher: CRC Press
Release Date : 2015-03-16
Statistical Reinforcement Learning written by Masashi Sugiyama and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-16 with Business & Economics categories.
Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.