The Book contains 17 in total articles that are accepted and published in the Special Issue “Computational Methods and Application in Machine Learning, 2023” of the MDPI Mathematics journal. The articles cover a wide range of topics with respect to the theory and applications of the computational method in machine learning. These keywords include artificial intelligence big data and analysis, machine learning, deep learning, natural language understanding, pattern recognition, computer vision, information retrieval, data mining, bioinformatics and biomedical applications, reinforcement learning, multimedia analysis and retrieval, multimodal representation learning, feature selection, clustering, etc.

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more.

This is the fourth edition of the Mathematics handbook for science and engineering (BETA). Compared to the previous editions a number of additions and corrections have been made. The Mathematics handbook covers basic areas of mathematics, numerical analysis, probability and statistics and various applications. The handbook is intended for students and teachers of mathematics, science and engineering and for profession­ als working in these areas. The aim of the handbook is to provide useful informa­ tion in a lucid and accessible form in a moderately large volume. The handbook concentrates on definitions, results, formulas, graphs, figures and tables and emphasizes concepts and methods with applications in technology and science. The Mathematics handbook is organised in 19 chapters starting with basic concepts in discrete mathematics and ending with chapters on probability and statistics and a miscellaneous chapter. Crossreferences and an extensive index help the user to find required information. We have not included numerical tables of functions which are available on most scientific calculators and pocket computers. We have treated one variable and multi variable calculus in different chapters, because students, usu­ ally, meet these areas in different courses. In formulating theorems and results sometimes all assumptions are not explicitely stated.

This book features selected papers from the 10th International Conference on Mathematics and Computing (ICMC 2024), held at Kalasalingam Academy of Research and Education (KARE), Krishnankoil, India during 2 – 7 January 2024. It covers recent advances in the field of mathematics, statistics, and scientific computing. The book presents innovative work by leading academics, researchers, and experts from industry in mathematics, statistics, cryptography, network security, cyber security, machine learning, data analytics and blockchain technology in computer science and information technology. The book is divided into two volumes.

A two-week summer short course entitled Current Statistical Methods in Geology supported by the National Science Foundation was held at the University of Illinois at Chicago Circle in Chicago, Illinois from June 19 to June 30, 1972. The aim of the short course was to bridge the gap between the traditional first courses in sta­ tistics offered at most educational institutions and geostatistics as it is being developed by geologists and statisticians engaged in the application of statistics in geology. The course was intended for geology college teachers who were either then teaching or preparing to teach a course within their department dealing with computer ap­ plications and the use of statistical methods in geology. This book arose out of the class notes which were prepared by the course director and the invited lecturers. We are grateful to the 28 teachers who attended for their enthu­ siastic interest and thoughtful responses to the many statistical concepts presented to them as geologists during the two weeks of the course. I am deeply grateful to my graduate assistants, Richard Kolb and Andrea Krivz, for the long hours spent in collating the course mater­ ials, testing the various computer programs, and instructing the par­ ticipants in the use of computer BASIC.

Written by a leading researcher this book presents an introduction to Stochastic Petri Nets covering the modeling power of the proposed SPN model, the stability conditions and the simulation methods. Its unique and well-written approach provides a timely and important addition to the literature. Appeals to a wide range of researchers in engineering, computer science, mathematics and OR.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size.

In 1960, Dr. George Deacon ofthe National Institute ofOceanography in England organized a meeting in Easton, Maryland that summarized the state of our understanding at that time of ocean wave statistics and dynamics. It was a pivotal occasion: spectral techniques for wave measurement were beginning to be used, wave-wave interactions hadjust been discovered, and simple models for the growth of waves by wind were being developed. The meeting laid the foundation for much work that was to follow, but one could hardly have imagined the extent to which new techniques of measurement, particularly by remote sensing, new methods of calculation and computation, and new theoretical and laboratory results would, in the following twenty years, build on this base. When Gaspar Valenzuela of the V. S. Naval Research Laboratory perceived that the time was right for a second such meeting, it was natural that Sir George Deacon would be invited to serve as honorary chairman for the meeting, and the entire waves community was delighted at his acceptance. The present volume contains reviewed and edited papers given at this second meeting, held this time in Miami, Florida, May 13-20, 1981, with the generous support of the Office of Naval Research, the National Aeronautics and Space Administration, and the National Oceanic and Atmospheric Administration.

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages.

Queuing Theory and Telecommunications: Networks and Applications provides some fundamental knowledge in queuing theory, as well as essential analytical methods and approaches to be employed to evaluate and design telecommunication networks.

Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.

Within the realm of quantitative geography, systems modelling is specifically concerned with understanding those relationships that influence the attributes of phenomena located in space and time. The intention is to replicate the main processes influencing a system's behaviour and, thereby, assist its manage­ ment through a capability to estimate future change. Over the last few decades, one of the major institutional initiatives for promoting such research has been provided by specialised Study Groups and Commissions established by the International Geographical Union (IGU). These scholarly networks have aimed to co-ordinate international research agendas for geographical systems model­ ling and their activities have been recorded in both edited volumes (Fischer and Getis, 1997) and special issues of learned journals (Wilkinson and Boots, 2000; Leung and Okabe, 2001).