Algorithmic Aspects of Machine Learning

Informationen zum Autor Ankur Moitra is the Rockwell International Associate Professor of Mathematics at Massachusetts Institute of Technology. He is a principal investigator in the Computer Science and Artificial Intelligence Lab (CSAIL), a core member of the Theory of Computation Group, Machine Learning@MIT, and the Center for Statistics. The aim of his work is to bridge the gap between theoretical computer science and machine learning by developing algorithms with provable guarantees and foundations for reasoning about their behavior. He is a recipient of a Packard Fellowship, a Sloan Fellowship, an National Science Foundation (NSF) CAREER Award, an NSF Computing and Innovation Fellowship and a Hertz Fellowship. Klappentext Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit. Zusammenfassung Machine learning is reshaping our everyday life. This book explores the theoretical underpinnings in an accessible way! offering theoretical computer scientists an introduction to important models and problems and offering machine learning researchers a cutting-edge algorithmic toolkit. Inhaltsverzeichnis 1. Introduction; 2. Nonnegative matrix factorization; 3. Tensor decompositions - algorithms; 4. Tensor decompositions - applications; 5. Sparse recovery; 6. Sparse coding; 7. Gaussian mixture models; 8. Matrix completion.

Product details

• Authors: Ankur Moitra, Ankur (Massachusetts Institute of Technolo Moitra, Ankur (Massachusetts Institute of Technology) Moitra
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Hardback
• Released: 31.08.2018
• EAN: 9781107184589
• ISBN: 978-1-107-18458-9
• No. of pages: 158
• Subjects: Guides; Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, algorithms and data structures, Probability & statistics, Algorithms & data structures, Probability and statistics