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Authors: Mohammed Nurudeen (2024) - This book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to machine learning using Python. The primary goal is to take readers from the fundamental concepts of machine learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the data science community.
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Authors: Jayaraman Valadi (2024) - The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field
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Authors: Daniel Alpay (2024) - This text presents a collection of mathematical exercises with the aim of guiding readers to study topics in statistical physics, equilibrium thermodynamics, information theory, and their various connections. It explores essential tools from linear algebra, elementary functional analysis, and probability theory in detail and demonstrates their applications in topics such as entropy, machine learning, error-correcting codes, and quantum channels. The theory of communication and signal theory are also in the background, and many exercises have been chosen from the theory of wavelets and machine learning. Exercises are selected from a number of different domains, both theoretical and mor...
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Authors: Umberto Michelucci (2024) - This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplinessuch as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity.
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Authors: Charu C. Aggarwal (2024) - The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners
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Authors: Kevin P. Murphy (2022) - "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"
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Authors: Otávio Santana (2024) - The book is divided into four parts, covering essential NoSQL concepts, Java principles, Jakarta EE integration, and the integration of NoSQL databases into enterprise architectures. Readers will explore NoSQL databases, comparing their strengths and use cases. They will then master Java coding principles and design patterns necessary for effective NoSQL integration. The book also discusses the latest Jakarta EE specifications, enhancing readers' understanding of Jakarta's role in data storage and retrieval. Finally, readers will learn to implement various NoSQL databases into enterprise-grade solutions, ensuring security, high availability, and fault tolerance.
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Authors: Jeff Friesen (2024) - Sharpen your Java skills and boost your potential as an IT specialist. This book introduces you to the basic Java features and APIs needed to prepare for a career in programming and development. You'll first receive an introduction to Java and then explore language features ranging from comments though exception/error handling, focusing mainly on language syntax and a few select syntax-related APIs. This constitutes the heart of the book, and you'll use these building blocks to construct simple Java programs, and learn where Java's implementations of expressions (and operators), and statements diverge from other languages.
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Authors: John Tuhao Chen (2024) - "Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms...
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Authors: Dinh The Luc (2016) - This book introduces the reader to the field of multiobjective optimization through problems with simple structures, namely those in which the objective function and constraints are linear. Fundamental notions as well as state-of-the-art advances are presented in a comprehensive way and illustrated with the help of numerous examples. Three of the most popular methods for solving multiobjective linear problems are explained, and exercises are provided at the end of each chapter, helping students to grasp and apply key concepts and methods to more complex problems. The book was motivated by the fact that the majority of the practical problems we encounter in management science, engineer...
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Authors: Trần Vũ Thiệu (2011) - Lý thuyết chung về bài toán tối ưu, giải tích lồi, điều kiện tối ưu, bài toán ngẫu hứng. Phương pháp tìm cực tiểu không ràng buộc và có ràng buộc, phương pháp không dùng đạo hàm, phương pháp gradient, phương pháp tuyến tính hoá..
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Authors: Đào Hữu Hồ (2007) - Một số khái niệm, kết quả cơ bản của xác suất và thống kê xã hội được trình bày qua các bài toán giải tích tổ hợp, phép thử và biến cố, biến ngẫu nhiên, hàm phân phối, các số đặc trưng của biến ngẫu nhiên, lí thuyết mẫu, ước lượng đơn giản, bài toán kiểm định giả thiết đơn giản, tương quan và hồi qui..
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Authors: Patrice Bertail (2006) - Gives an account of the developments in the field of probability and statistics for dependent data. This book covers a range of topics from Markov chain theory and weak dependence with an emphasis on some developments on dynamical systems, to strong dependence in times series and random fields
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Authors: Y. Suhov; M. Kelbert (2005) - Probability and Statistics are as much about intuition and problem solving, as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature.
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Authors: Morris H. DeGroot (1986) - The revision of this well-respected text presents a balance of the classical and Bayesian methods. The theoretical and practical sides of both probability and statistics are considered. New content areas include the Vorel- Kolmogorov Paradox, Confidence Bands for the Regression Line, the Correction for Continuity, and the Delta Method.
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Authors: Mark Zegarelli (2022) - Offers explanations of concepts such as whole numbers, fractions, decimals, and percents, and covers advanced topics including imaginary numbers, variables, and algebraic equations
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Authors: John J. Kinney (2009) - This handy book contains introductory explanations of the major topics in probability and statistics, including hypothesis testing and regression, while also delving into more advanced topics such as the analysis of sample surveys, analysis of experimental data, and statistical process control. The book recognizes that there are many sampling techniques that can actually improve on simple random sampling, and in addition, an introduction to the design of experiments is provided to reflect recent advances in conducting scientific experiments. This blend of coverage results in the development of a deeper understanding and solid foundation for the study of probability statistics. --
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Authors: Robert Bartoszynski (2008) - Provides a mathematical framework that permits students to carry out various procedures using any number of computer software packages as opposed to relying on one particular package
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Authors: John Tabak (2004) - A primer on probability and statistics that includes a chronology of notable events, a glossary of terms, and an array of sources for further research
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Authors: Nitis Mukhopadhyay (2000) - This textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts - reinforcing important ideas and emphasizing special techniques with drills and boxed summaries
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