This volume builds upon the statistical and computational foundations established in Volume 1, advancing readers into modern machine learning, high-dimensional inference, and specialized data analysis domains. This volume focuses on scalable modeling techniques, algorithmic learning, and contemporary applications that define today's data science practice.
Beginning with classification methods and model selection techniques, the volume systematically introduces tree-based algorithms, ensemble learning, and penalized regression approaches for high-dimensional data. Both supervised and unsupervised learning paradigms are covered, including clustering, factor analysis, survival analysis, and image data analysis, providing breadth across methodological domains.
A distinguishing feature of this volume is its integration of statistical principles with modern machine learning, offering readers a unified perspective on inference, regularization, generalization, and interpretability. Advanced chapters explore text analytics, deep learning from a statistical viewpoint, spatial data analysis, and portfolio risk modeling, highlighting real-world relevance across finance, public policy, and urban analytics.
Throughout the volume, theory is reinforced with practical implementations, case studies, and data-driven illustrations, enabling readers to transition from classical analytics to advanced data science workflows. This volume is intended for students, researchers, and practitioners who have mastered foundational concepts and seek to apply cutting-edge analytical techniques in complex, real-world settings.
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