Machine Learning

Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

Addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. Comprehensive overview of novel approaches to machine learning research that consider resource constraints and application of the described methods.

How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

Survey the nascent literature on machine learning in the study of financial markets. Highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

Everything you really need to know in Machine Learning in a hundred pages!

This book is an introduction to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making.

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.

This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance.

This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.

This practical book offers clear, example-rich explanations of how Large Language Models (LLM) work, how you can interact with them, and how to integrate LLMs into your own applications.

The Practical, Step-by-Step Guide to Using Large Language Models (LLM) at Scale in Projects and Products. Clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.

The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field.

This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases.

A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. This book presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures.