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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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Economics

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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. From the reviews of the first edition: "The book starts … with five eye-catching pages that reproduce a student’s handwritten notes for the examination that is based on this book. … The material is well presented with a good balance between theoretical and applied aspects. … The book is an excellent demonstration of the power of stochastics … . The author’s goal is well achieved: this book can satisfy the needs of different groups of readers … . " (Jordan Stoyanov, Journal of the Royal Statistical Society, Vol. 168 (4), 2005).

Private finance --- Mathematical statistics --- AA / International- internationaal --- 305.970 --- 306.4 --- 305.91 --- 305.7 --- 333.605 --- 303.5 --- 339.42 --- Finance --- -Finance --- -332.0151 --- Funding --- Funds --- Economics --- Currency question --- 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). --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles. --- Econometrie van het gedrag van de financiële tussenpersonen. Monetaire econometrische modellen. Monetaire agregaten. vraag voor geld. Krediet. Rente. --- Nieuwe financiële instrumenten. --- 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). --- Financiële analyse. --- Statistical methods --- Mathematical models --- Mathematical models. --- Statistical methods. --- 332.0151 --- 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 --- Econometrics. --- Statistics . --- Economics, Mathematical . --- Finance. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Quantitative Finance. --- Finance, general. --- Mathematical economics --- Econometrics --- Mathematics --- Statistical analysis --- Statistical data --- Statistical science --- Economics, Mathematical --- Statistics --- Methodology --- Finances