Understanding Machine Learning: From Theory to Algorithms

The book Understanding Machine Learning: From Theory to Algorithms introduces foundational principles of machine learning—formalising concepts like PAC learning, generalisation and convexity—then develops practical algorithms such as stochastic gradient descent, neural networks and structured output learning while addressing computational complexity and emerging theoretical ideas. It targets advanced undergraduates and beginning graduate students in computer science, maths, engineering or statistics.

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