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This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.

Statistics. --- Machine learning. --- Statistical Theory and Methods. --- Machine Learning. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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Now in its fourth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods of evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to given problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic. For this new edition the book has been updated and extensively revised and now includes several new aspects, e.g. new chapters on long memory models, copulae and CDO valuation. Practical exercises with solutions have also been added. Both R and Matlab Code, together with the data, can be downloaded from the book’s product page and www.quantlet.de.

Statistics. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Quantitative Finance. --- Finance/Investment/Banking. --- Finance. --- Economics --- Statistique --- Finances --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Economics, Mathematical. --- Finance, general. --- Mathematical economics --- Econometrics --- Funding --- Funds --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Methodology --- Statistics for Business, Management, Economics, Finance, Insurance. --- Finance --- Statistical methods. --- Statistics . --- Economics, Mathematical .

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Statistics of Financial Markets offers a vivid yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods to evaluate option contracts, to analyse financial time series, to select portfolios and manage risks making realistic assumptions of the market behaviour. The focus is both on fundamentals of mathematical finance and financial time series analysis and on applications to given problems of financial markets, making the book the ideal basis for lectures, seminars and crash courses on the topic. For the second edition the book has been updated and extensively revised. Several new aspects have been included, among others a chapter on credit risk management.

Finance --- Statistical methods. --- Mathematical models. --- Econometrics. --- Statistics. --- Finance. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Quantitative Finance. --- Finance, general. --- Funding --- Funds --- Economics --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Economics, Mathematical --- Statistics --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Methodology --- Finance - Statistical methods --- Finance - Mathematical models --- 332.015195 --- 303.5 --- 305.7 --- 305.91 --- 305.970 --- 306.4 --- 333.605 --- 339.42 --- AA / International- internationaal --- 336.7 --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 336.7 Geldwezen. Kredietwezen. Bankwezen. Financien. Monetaire econonomie. Beurswezen --- Geldwezen. Kredietwezen. Bankwezen. Financien. Monetaire econonomie. Beurswezen --- Mathematical models --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Econometrie van het gedrag van de financiële tussenpersonen. Monetaire econometrische modellen. Monetaire agregaten. vraag voor geld. Krediet. Rente --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots --- Correlatie, regressie (toegepaste statistiek) --- Nieuwe financiële instrumenten --- Financiële analyse --- Economics, Mathematical.