Item #:
23533819
Bandit Algorithms
90% of respondents would recommend this to a friend
Item #:
23533819
NZD 132
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This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings
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What Stands Out
Comprehensive Coverage
The first edition offers extensive insights into bandit algorithms, covering essential theories and applications, making it ideal for researchers and practitioners looking to deepen their understanding in this specialized field.
Practical Applications
This book not only discusses theoretical concepts but also illustrates practical implementations, thereby bridging the gap between theory and real-world use cases for data-driven decisions in various industries.
Expert Authors
Written by recognized experts in the field, the book ensures high-quality content, bringing authoritative knowledge and trusted methodologies that enhance the reader's learning and application of bandit algorithms.
Product Details
- Introduces the concept of bandit algorithms
- Explains how to apply bandit algorithms in various scenarios
- Provides practical examples and case studies
- Covers both contextual and non-contextual bandit problems
- Written by experts in the field
- Offers a comprehensive guide to bandit algorithms
| Publisher | Cambridge University Press |
| Publication date | September 10, 2020 |
| Edition | 1st |
| Language | English |
| Print length | 536 pages |
| ISBN-10 | 1108486827 |
| ISBN-13 | 978-1108486828 |
| Item Weight | 2.36 pounds (1.07 kg) |
| Dimensions | 7 x 1.25 x 9.5 inches (17.8 x 3.2 x 24.1 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to understand and implement bandit algorithms in real-world applications.
-
Machine Learning Practitioners
Beneficial for practitioners wanting to enhance their understanding of algorithmic decision-making in uncertain environments.
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Researchers
Great for researchers interested in the theoretical foundations and applications of bandit algorithms in various fields.
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Casual Learners
Not suitable for those seeking an introductory overview without deep technical details or mathematical rigor.
Product Description
Bandit Algorithms
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Product Buying Guide
Decision-making in machine learning under uncertainty is a complex task. Bandit Algorithms 1st Edition provides a comprehensive and rigorous introduction to the multi-armed bandit problem, making it an essential resource for researchers and graduate students in various fields.
Product Specifications
- Csaba Szepesvari and Tor Lattimore
- Hardcover
- English
- Cambridge University Press
- November 8, 2018
- 558 pages
- 6.9 x 1.1 x 9.8 inches
Key Features
- Examines major settings including stochastic, adversarial, and Bayesian frameworks.
- Focuses on providing both mathematical intuition and carefully worked proofs.
- Special attention to linear bandits, combinatorial bandits, ranking, non-stationary problems, Thompson sampling, and pure exploration.
- Offers insights into partial monitoring and learning in Markov decision processes.
Usage Scenarios
- An essential reference for established researchers due to its comprehensive coverage and rigorous approach.
- A helpful resource for graduate students in computer science, engineering, statistics, applied mathematics, and economics.
Usage Scenarios
- Multi-Armed Bandit Allocation Indices by John Myles White
- Bandit Algorithms for Website Optimization by John Myles White
Some User Review
- The book provides a deep understanding of bandit algorithms with a good balance between theory and applications.
- A valuable resource for anyone interested in understanding decision-making under uncertainty in the context of machine learning.
Competitors
- The book offers competitive pricing compared to similar publications in the field, providing excellent value for its comprehensive coverage.
Buying Considerations
- Published by Cambridge University Press, known for its high-quality academic publications.
- Offers an in-depth examination of the multi-armed bandit problem and its various settings, making it a suitable choice for those seeking comprehensive coverage.
- Positive user reviews highlight the value and depth of content provided in this resource.
Conclusion
Bandit Algorithms 1st Edition is a valuable resource that offers a comprehensive and rigorous introduction to the multi-armed bandit problem, making it an essential reference for researchers and a helpful resource for graduate students in related disciplines.
View LessDecision-making in machine learning under uncertainty is a complex task. Bandit Algorithms 1st Edition provides a comprehensive and rigorous introduction to the multi-armed bandit problem, making it an essential resource for researchers and graduate students in various fields. Continue Reading
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Pros
- In-depth theoretical insights
- Practical algorithms explained
- Clear examples and illustrations
- Well-structured chapters
- Strong community and resources
Cons
- Advanced for beginners
Product Price History
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NZD 132
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Features & Benefits
- Book is focused on decision-making in the face of uncertainty
- Introduces Multi-Armed Bandit Model
- Suitable for researchers and graduate students
- Content includes linear and combinatorial bandits, ranking, non-stationary problems
- Provides introduction to partial monitoring and learning in Markov decision processes
- Covers stochastic, adversarial, and Bayesian frameworks
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