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Understanding Machine Learning: From Theory to Algorithms

Users: 3 - Average Rating: 5.00

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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Type:
Book
Area:
Machine Learning
Target Group:
Advanced
DOI:
https://doi.org/10.1017/CBO9781107298019
Link:
https://www.cs.huji.ac.il/w~shais/UnderstandingMachineLearning/index.html
Author of the review:
Spiros Sirmakessis
University of Peloponnese


Reviews

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Giulia Cademartori


The book starts with a concise and clear introduction to statistical machine learning and then consistently connects those concepts to the main ML algorithms. This is definitely not a "how to" book, but rather a "what and why" book, focused on understanding principles and connections between ML algorithms.

Danilo Franco


This is a good textbook for a theorical introduction and a deeper understanding to machine learning and the main learning techniques with also pseudo-codes implementation of the algorithms. The reading could be difficult if you don’t have a good mathematical background!

Joana Lopes


O enjoyed the part about the computational complexity of ML algorithms.
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