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Understanding Machine Learning: From Theory to Algorithms
01-03-2018, 09:32 AM
Post: #1
Understanding Machine Learning: From Theory to Algorithms
Understanding Machine Learning
Machine learning is one of the fastest growing areas of computer science,with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous text-books. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks,and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at The Hebrew University, Israel.
Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.
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07-03-2018, 12:30 AM
Post: #2
RE: Understanding Machine Learning: From Theory to Algorithms
I've been following professor Ben-David's work on machine learning and I'm impressed to say the least. He's on the cutting edge of this technology. This is a fantastic read as well.

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