The Mathematics of the Models of Reference is a detailed exposition of the modeling of physical and informational reality pursued by iLabs Milan ( www.ilabs.it ), a private research lab in applied Artificial Intelligence. Based on an original approach to cellular automata theory, this book includes an array of axiomatic formal theories, ranging from a discrete, mereological model of the structure of space-time, to non-standard computation and recursion theory. The appendices to the volume explain the applications of the theory in the algorithmic recapture of a variety of physical, biological, and cognitive phenomena. Francesco Berto Logic & Formal Modeling @ iLabs PhD in Philosophy, has studied at the University of Notre Dame (Indiana, USA), at the Sorbonne-Ecole Normale Superieure of Paris, and is currently lecturer at the University of Aberdeen. He has published various papers and monographs in ontology and the philosophy of logic. Gabriele Rossi Director of iLabs A.I. Department @ iLabs Has a degree in Economic and Social Disciplines at the Bocconi University in Milan and is CEO of Diagramma, a leading company in insurance software applications. Expert in Artificial Intelligence, in 2007 he has co-authored with Antonella Canonico the book Semi-Immortality, a manifesto of European transhumanism. Jacopo Tagliabue Chief Scientist for Qualitative Modeling @ iLabs A PhD student with a degree in Philosophy at the University San Raffaele of Milan, has studied Economics at LSE, Statistics at New York University, and Complex Systems at the Santa Fe Institute. He has published papers in ontology and non-standard computation.

Assumes only a familiarity with algebra at the beginning graduate level; Stresses applications to algebra; Illustrates several of the ways Model Theory can be a useful tool in analyzing classical mathematical structures

A textbook for a first-year PhD course in mathematics for economists and a reference for graduate students in economics.

Learn how quantitative models can help fight client problems head-on Before financial problems can be solved, they need to be fully understood. Since in-depth quantitative modeling techniques are a powerful tool to understanding the drivers associated with financial problems, one would need a solid grasp of these techniques before being able to unlock their full potential of the methods used. In The Mathematics of Financial Models, the author presents real world solutions to the everyday problems facing financial professionals. With interactive tools such as spreadsheets for valuation, pricing, and modeling, this resource combines highly mathematical quantitative analysis with useful, practical methodologies to create an essential guide for investment and risk-management professionals facing modeling issues in insurance, derivatives valuation, and pension benefits, among others. In addition to this, this resource also provides the relevant tools like matrices, calculus, statistics and numerical analysis that are used to build the quantitative methods used. Financial analysts, investment professionals, risk-management professionals, and graduate students will find applicable information throughout the book, and gain from the self-study exercises and the refresher course on key mathematical topics. Equipped with tips and information, The Mathematics of Financial Models Provides practical methodologies based on mathematical quantitative analysis to help analysts, investment and risk-management professionals better navigate client issues Contains interactive tools that demonstrate the power of analysis and modeling Helps financial professionals become more familiar with the challenges across a range of industries Includes a mathematics refresher course and plenty of exercises to get readers up to speed The Mathematics of Financial Models is an in-depth guide that helps readers break through common client financial problems and emerge with clearer strategies for solving issues in the future.

Model categories are used as a tool for inverting certain maps in a category in a controllable manner. They are useful in diverse areas of mathematics. This book offers a comprehensive study of the relationship between a model category and its homotopy category. It develops the theory of model categories, giving a development of the main examples.

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 computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

This is an up-to-date textbook of model theory taking the reader from first definitions to Morley's theorem and the elementary parts of stability theory. Besides standard results such as the compactness and omitting types theorems, it also describes various links with algebra, including the Skolem-Tarski method of quantifier elimination, model completeness, automorphism groups and omega-categoricity, ultraproducts, O-minimality and structures of finite Morley rank. The material on back-and-forth equivalences, interpretations and zero-one laws can serve as an introduction to applications of model theory in computer science. Each chapter finishes with a brief commentary on the literature and suggestions for further reading. This book will benefit graduate students with an interest in model theory.