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Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ?elds as computer vision, natural language …
A new generation of students needs to learn about the interplay between computer science and statistics but do not wish to learn a heavy mathematical overhead. Elements of Pattern …
This book is an investigation into the problems of generating natural language utterances to satisfy specific goals the speaker has in mind. It is thus an ambitious and significant …
Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces …
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, …
Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including …
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. …
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and …
Robust statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as in the …
The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook …