By Marco Scutari, Mauro Malvestio
March 31, 2023
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life, yet software engineering has played a remarkably small role compared to other disciplines. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data ...
By Mark Stamp
September 27, 2022
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 learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove ...
By Yuri S. Popkov, Alexey Yu. Popkov, Yuri A. Dubnov
August 09, 2022
Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum ...
By Uday Kamath, Kenneth Graham, Wael Emara
May 25, 2022
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 Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. ...
By Momiao Xiong
March 08, 2022
Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying ...
By Shalom Lappin
April 27, 2021
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 some of these applications, deep learning models now approach or surpass human performance. While the success of this ...
By Craig Friedman, Sven Sandow
November 25, 2019
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 who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not ...
By Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
November 14, 2019
"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 topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or ...
By A.C. Faul
August 07, 2019
The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the ...
By Simon Rogers, Mark Girolami
July 26, 2016
"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, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes ...
By Masashi Sugiyama
June 05, 2015
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 numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for ...
By Stephen Marsland
November 17, 2014
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning...
