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Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Advances in Big Data Analytics : Theory, Algorithms and Practices
Provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.
Applications of Specification and Design Languages for SoCs : Selected papers from FDL 2005
This book provides detailed insights into recent works dealing with a large spectrum of issues in system-on-chip design, namely: assertion-based design, mapping on network-on-chip architectures, use of C/C++/SystemC design methodologies, hardware/software integration, mixing heterogeneous models of computation, analog/mixed-signal/mixed-technology system design and verification, UML/XML-based synthesis of analog and mixed-signal systems, UML to VHDL mapping, UML-based performance modeling, model transformation and formal verification, real-time system models, and Model Driven Architecture.
