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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Bandit Algorithms (Hardcover)
Loot Price: R1,377
Discovery Miles 13 770
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Bandit Algorithms (Hardcover)
Expected to ship within 9 - 15 working days
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Decision-making in the face of uncertainty is a significant
challenge in machine learning, and the multi-armed bandit model is
a commonly used framework to address it. This comprehensive and
rigorous introduction to the multi-armed bandit problem examines
all the major settings, including stochastic, adversarial, and
Bayesian frameworks. A focus on both mathematical intuition and
carefully worked proofs makes this an excellent reference for
established researchers and a helpful resource for graduate
students in computer science, engineering, statistics, applied
mathematics and economics. Linear bandits receive special attention
as one of the most useful models in applications, while other
chapters are dedicated to combinatorial bandits, ranking,
non-stationary problems, Thompson sampling and pure exploration.
The book ends with a peek into the world beyond bandits with an
introduction to partial monitoring and learning in Markov decision
processes.
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