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Algorithmic aspects of machine learning / Ankur Moitra, Massachusetts Institute of Technology.

By: Material type: TextTextLanguage: English Publication details: Cambridge, United Kingdom ; New York, NY, USA : Cambridge University Press, 2018.Description: pages cmISBN:
  • 9781107184589 (hardback)
  • 9781316636008 (paperback)
Subject(s): DDC classification:
  • 006.3/1015181 23 MOI
Contents:
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.
Summary: This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning Read more
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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.


This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning Read more

Includes bibliographical references.

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