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Algorithms for Decision Making
This textbook provides a broad introduction to algorithms for decision-making under uncertainty, covering essential mathematical problem formulations and solving algorithms.
Algorithms for Decision Making
artikal br.: 89184769

Algorithms for Decision Making

artikal br.: 89184769

RSD 12444

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This textbook provides a broad introduction to algorithms for decision-making under uncertainty, covering essential mathematical problem formulations and solving algorithms.
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What Stands Out

Data-Driven Insights
Utilizes advanced algorithms to transform raw data into actionable insights, enhancing decision-making accuracy and speed for businesses in competitive environments.
User-Friendly Interface
Designed with a simple interface, making it accessible for users with varying technical expertise, thereby promoting widespread adoption and effective utilization.
Real-Time Analytics
Provides real-time data processing, allowing organizations to make informed decisions swiftly, reducing risks and maximizing opportunities in dynamic markets.

Детаљи о производу

Shop Algorithms for Decision Making online at a best price in Serbia. 0262047012
  • A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Publisher The MIT Press
Publication date August 16, 2022
Language English
Print length 700 pages
ISBN-10 0262047012
ISBN-13 978-0262047012
Item Weight 2.72 pounds (1.23 kg)
Dimensions 8.27 x 1.5 x 9.25 inches (21 x 3.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for professionals who need to implement algorithms in data analysis and decision-making processes.

  • Business Analysts

    Useful for analysts aiming to improve decision processes within organizations using algorithmic strategies.

  • Students in AI

    Beneficial for students pursuing studies in artificial intelligence and seeking practical applications of decision-making algorithms.

Not Suitable For
  • Casual Users

    Not suitable for individuals without technical background or interest in algorithms and data analysis.

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Прос

  • Easy to understand concepts
  • Practical real-world examples
  • Well-organized structure
  • Engaging writing style
  • Comprehensive coverage of topics

Цонс

  • Some sections could use more examples.

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