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"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, …
Providing an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic …
Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep …
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With …
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to …
Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress …
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including …
Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer …
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: …
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is …