9786209697180 - Machine Learning Algorithms for Complex Decisions de L, Sharmila; M, Anandasimhan; R, Umamageshwari (11 resultados)

Condición: New.

Taschenbuch. Condición: Neu. MACHINE LEARNING ALGORITHMS FOR COMPLEX DECISIONS | Sharmila L (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209697180 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As machine learning continues to evolve, its role in decision making will expand beyond optimization toward understanding, foresight, and collaboration. The goal is not perfect prediction or automated control, but wiser decisions that acknowledge uncertainty, respect human dignity, and adapt to an ever-changing world. By grounding machine learning in sound theory, ethical reflection, and practical insight, we move closer to decision systems that serve not only efficiency but also equity, sustainability, and collective well-being.As the scope of machine learning in complex decision making continues to expand, it becomes increasingly important to reflect on the deeper implications of delegating elements of judgment to computational systems. Decisions are not isolated technical events; they are embedded within cultural norms, institutional structures, and human experiences. Machine learning systems, no matter how advanced, operate within these contexts and inevitably influence them. This reciprocal relationship means that the impact of algorithmic decision making extends beyond efficiency gains into the shaping of behaviours, incentives, and expectations across societies. 132 pp. Englisch.

Paperback. Condición: new. Paperback. As machine learning continues to evolve, its role in decision making will expand beyond optimization toward understanding, foresight, and collaboration. The goal is not perfect prediction or automated control, but wiser decisions that acknowledge uncertainty, respect human dignity, and adapt to an ever-changing world. By grounding machine learning in sound theory, ethical reflection, and practical insight, we move closer to decision systems that serve not only efficiency but also equity, sustainability, and collective well-being.As the scope of machine learning in complex decision making continues to expand, it becomes increasingly important to reflect on the deeper implications of delegating elements of judgment to computational systems. Decisions are not isolated technical events; they are embedded within cultural norms, institutional structures, and human experiences. Machine learning systems, no matter how advanced, operate within these contexts and inevitably influence them. This reciprocal relationship means that the impact of algorithmic decision making extends beyond efficiency gains into the shaping of behaviours, incentives, and expectations across societies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As machine learning continues to evolve, its role in decision making will expand beyond optimization toward understanding, foresight, and collaboration. The goal is not perfect prediction or automated control, but wiser decisions that acknowledge uncertainty, respect human dignity, and adapt to an ever-changing world. By grounding machine learning in sound theory, ethical reflection, and practical insight, we move closer to decision systems that serve not only efficiency but also equity, sustainability, and collective well-being.As the scope of machine learning in complex decision making continues to expand, it becomes increasingly important to reflect on the deeper implications of delegating elements of judgment to computational systems. Decisions are not isolated technical events; they are embedded within cultural norms, institutional structures, and human experiences. Machine learning systems, no matter how advanced, operate within these contexts and inevitably influence them. This reciprocal relationship means that the impact of algorithmic decision making extends beyond efficiency gains into the shaping of behaviours, incentives, and expectations across societies.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.