Category: Modelling and Optimization

Machine Learning and AI for Developing Business Strategies and Real-world Use Cases


Enterprises and institutions seek to enhance efficiency and outcomes through machine learning (ML). This paper explores ML’s application in optimization, emphasizing problem definition, data accumulation, feature engineering, model selection, training, evaluation, optimization, deployment, and monitoring. ML provides data-driven insights revolutionizing decision-making and operations, driving innovation and adaptation to market dynamics.… Read More Machine Learning and AI for Developing Business Strategies and Real-world Use Cases

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Surrogate Modelling: Data-driven Models for Machine Learning and Optimization


This article introduces surrogate modelling, creating data-driven models for machine learning and optimization applications, and gives examples of their applications. Furthermore, it presents how to construct surrogate models, lists common surrogate modelling methods, introduces a Python library consisting of implementation of these methods. The outline of this article is: Why Do We Need Surrogate Models… Read More Surrogate Modelling: Data-driven Models for Machine Learning and Optimization

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