MODERN MACHINE LEARNING MODELS: CLASSIFICATION, PRINCIPLES, AND APPLICATION AREAS
Keywords:
machine learning, supervised learning, neural networks, interpretability, robustness, classification, applied problemsAbstract
The article examines modern machine learning models, their classification based on architectural and functional features, as well as key principles of their design and application. Theoretical foundations of supervised, unsupervised, and reinforcement learning algorithms are outlined. A comparative analysis of their effectiveness in solving applied problems in fields such as healthcare, finance, energy, industry, and education is provided. Methods of generalization and systematization of scientific publications were used, along with real-world examples of successful model implementations. Particular attention is paid to interpretability, resistance to overfitting, generalization capability, and adaptability to new data. The findings can be useful in the design of intelligent systems and the selection of optimal architectures for specific practical tasks.
