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This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Actuarial science --- Insurance --- Linear models (Statistics) --- Actuarial science. --- Statistics . --- Actuarial Sciences. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Matemàtica actuarial --- Assegurances de vida --- Estadística --- Xarxes neuronals (Informàtica) --- Models matemàtics

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Neural networks (Computer science) --- Design and construction. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Xarxes neuronals (Informàtica)

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Cutting stock problem. --- Cybernetics. --- Cibernètica --- Automàtica --- Cervells electrònics --- Comunicació --- Electrònica --- Anàlisi de sistemes --- Biònica --- Control automàtic --- Ordinadors --- Robòtica --- Sistemes autoorganitzatius --- Sistemes de control biològic --- Teoria de la informació --- Xarxes neuronals (Informàtica) --- Mechanical brains --- Control theory --- Electronics --- System theory --- Cutting problem --- Rational cutting problem --- Operations research

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This book covers both classical and modern models in deep learning. The chapters of this book span three categories: 1. The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. 2. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. 3. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The book is written for graduate students, researchers, and practitioners. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- NLP (neurolinguïstisch programmeren) --- KBS (knowledge based system) --- datamining --- programmeren (informatica) --- KI (kunstmatige intelligentie) --- data acquisition --- AI (artificiële intelligentie) --- Machine learning. --- Data mining. --- Artificial intelligence. --- Expert systems (Computer science). --- Natural language processing (Computer science). --- Machine Learning. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Knowledge Based Systems. --- Natural Language Processing (NLP). --- Xarxes neuronals (Informàtica) --- Aprenentatge automàtic --- Aprenentatge profund

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This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. It also explains how to construct n-gram variables from text data. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences; true to the book’s goal to facilitate the use of modern methods of data science in the field. Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science.

Machine learning --- Neural networks (Computer science) --- Statistical methods. --- Stata. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Learning, Machine --- Machine theory --- Machine learning. --- Social sciences—Statistical methods. --- Statistics. --- Statistics—Computer programs. --- Quantitative research. --- Statistical Learning. --- Machine Learning. --- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Statistical Software. --- Data Analysis and Big Data. --- Data analysis (Quantitative research) --- Exploratory data analysis (Quantitative research) --- Quantitative analysis (Research) --- Quantitative methods (Research) --- Research --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Aprenentatge automàtic --- Estadística matemàtica --- Xarxes neuronals (Informàtica) --- Stata