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A First Course in Machine Learning
"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, …
Deep Learning and Linguistic Representation
The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For …
Introduction to Machine Learning with Applications in Information Security
Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through …
Statistical Reinforcement Learning
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 …
Data Science and Machine Learning
"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of …
Computational Trust Models and Machine Learning
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including …
Sparse Modeling
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 …
Regularization, Optimization, Kernels, and Support Vector Machines
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single …
Utility-Based Learning from Data
Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker …