Understanding Machine Learning: From Theory to Algorithms (Shai Shalev-Shwartz, et al)

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Understanding Machine Learning: From Theory to Algorithms (Shai Shalev-Shwartz, et al)

Computer programs are used in machine learning to find significant patterns in large amounts of complex data. It is one of the branches of computer science that is expanding the quickest and has numerous uses.

The ideas guiding the automated learning technique and the factors supporting its use are explained in this book.

The authors make the area understandable for students and professionals in computer science, statistics, and engineering by delineating the "hows" and "whys" of the most significant machine-learning algorithms as well as their innate advantages and disadvantages.

This textbook's objective is to provide a principled introduction to machine learning and the algorithmic paradigms it offers. The theoretical foundations of machine learning and the mathematical derivations that turn these foundations into useful algorithms are extensively covered in this book.

The book covers a wide range of essential issues that have not been covered by prior textbooks after introducing the fundamentals of the area. The computational complexity of learning and the ideas of convexity and stability are discussed, along with crucial algorithmic paradigms like stochastic gradient descent, neural networks, and structured output learning. Emerging theoretical ideas like the PAC-Bayes approach and compression-based bounds are also covered.

The text makes the foundations and techniques of machine learning understandable to students and non-expert readers in statistics, computer science, mathematics, and engineering. It is intended for an advanced undergraduate or beginning graduate course.

Ebook Details

Published Date / Year
1st edition (January 1, 2015); eBook (Free Online Copy)
This copy is for personal use only. Not for distribution. Do not post.
410 pages
eBook Format
PDF (449 pages)

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