Moritz Hardt – författare
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4 produkter
4 produkter
Inbunden, Engelska, 2023
803 kr
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E-bok
Engelska, 2023818 kr
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Inbunden, Engelska, 2022
571 kr
Skickas inom 5-8 vardagar
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers
Inbunden, Engelska, 2026
456 kr
Kommande
The first comprehensive introduction to benchmarking, the engine behind progress in AIIn machine learning, researchers split their data into training and test sets, let model builders compete on the test set, and call it a benchmark. Statistical tradition prescribed locking test sets in a vault, but machine learning practitioners shared them freely. Benchmarking shouldn’t have worked, but it did, and the machine learning community never figured out the science behind it. How did benchmarking, despite its flaws, lead to advances in AI? In The Emerging Science of Machine Learning Benchmarks, Moritz Hardt investigates why benchmarking works, and what purpose it serves.Hardt draws on a growing body of work that has begun to lay out the science underpinning benchmarks; what emerges is a rich landscape of theoretical and empirical observations that can inform practitioners. He begins with the foundations, both mathematical and empirical, covering enough background material to make the book self-contained. He finds that model rankings, rather than model evaluation, are the primary scientific product of machine learning benchmarks. Turning to the challenges of benchmarking large language models, Hardt explains how benchmarks influence model training, complicating direct model comparisons. As model capabilities exceed those of human evaluators, researchers are running out of ways to test new models. If benchmarks are to serve us well in the future, we must place them on solid scientific ground. With this book, Hardt lays the foundation.