Advanced Machine Learning Techniques: Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. Global contributors cover theoretical foundation topics such as computational and statistical convergence rates, minimax estimation and concentration of measure. Advanced machine learning methods such as nonparametric density estimation, nonparametric regression, and Bayesian estimation, as well as advanced frameworks such as privacy, causality and stochastic learning algorithms are also included. Other methods covered include Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode, making this word an interdisciplinary guide that will appeal to post graduates interested in Computer Science, Artificial Intelligence, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources and Chemical Engineering. Contains contributions from the fields of data management research, climate change and resilience, insufficient data problem, and more Presents applied examples and case studies in each chapter, providing the reader with real-world scenarios for comparison Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees

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