Algorithmic Aspects of Machine Learning

'This book is a gem. It is a series of well-chosen and organized chapters, each centered on one algorithmic problem arising in machine learning applications. In each, the reader is lead through different ways of thinking about these problems, modeling them, and applying different algorithmic techniques to solving them. In this process, the reader learns new mathematical techniques from algebra, probability, geometry and analysis that underlie the algorithms and their complexity. All this material is delivered in a clear and intuitive fashion.' Avi Wigderson, Institute for Advanced Study, New Jersey

'A very readable introduction to a well-curated set of topics and algorithms. It will be an excellent resource for students and researchers interested in theoretical machine learning and applied mathematics.' Sanjeev Arora, Princeton University, New Jersey

'This text gives a clear exposition of important algorithmic problems in unsupervised machine learning including nonnegative matrix factorization, topic modeling, tensor decomposition, matrix completion, compressed sensing, and mixture model learning. It describes the challenges that these problems present, gives provable guarantees known for solving them, and explains important algorithmic techniques used. This is an invaluable resource for instructors and students, as well as all those interested in understanding and advancing research in this area.' Avrim Blum, Toyota Technical Institute at Chicago

'Moitra … has written a high-level, fast-paced book on connections between theoretical computer science and machine learning. … A main theme throughout the book is to go beyond worst-case analysis of algorithms. This is done in three ways: by probabilistic algorithms, by algorithms that are very efficient on simple inputs, and by notions of stability that emphasize instances of problems that have meaningful answers and thus are particularly important to solve. … Summing Up: Highly recommended.' M. Bona, Choice