This paper explores the application of machine learning in next-generation wireless networks. The authors discuss how machine learning can be used to support smart radio terminals, enabling them to autonomously access the most beneficial spectral bands, control transmission power, and adjust transmission protocols. They review the basic concepts of machine learning and propose their application in 5G networks, including cognitive radios, massive MIMO, femto/small cells, heterogeneous networks, smart grid, energy harvesting, and device-to-device communications. The paper discusses three main types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning is used for tasks such as channel estimation, spectrum sensing, and data detection in cognitive radio networks. Unsupervised learning is used for clustering in heterogeneous networks, access point association in WiFi networks, and load balancing in heterogeneous networks. Reinforcement learning is used for decision-making in dynamic environments, such as energy harvesting and distributed resource allocation in femto/small-cell networks. The paper also discusses the application of machine learning in various wireless communication scenarios, including the use of Q-learning for femto/small-cell networks and multi-armed bandits for device-to-device networks. The authors conclude that machine learning is a promising area for artificial intelligence aided networking research, and that it has the potential to significantly improve the performance of next-generation wireless networks. They also mention that future research should focus on the development of more efficient machine learning algorithms and their application in various wireless communication scenarios.This paper explores the application of machine learning in next-generation wireless networks. The authors discuss how machine learning can be used to support smart radio terminals, enabling them to autonomously access the most beneficial spectral bands, control transmission power, and adjust transmission protocols. They review the basic concepts of machine learning and propose their application in 5G networks, including cognitive radios, massive MIMO, femto/small cells, heterogeneous networks, smart grid, energy harvesting, and device-to-device communications. The paper discusses three main types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning is used for tasks such as channel estimation, spectrum sensing, and data detection in cognitive radio networks. Unsupervised learning is used for clustering in heterogeneous networks, access point association in WiFi networks, and load balancing in heterogeneous networks. Reinforcement learning is used for decision-making in dynamic environments, such as energy harvesting and distributed resource allocation in femto/small-cell networks. The paper also discusses the application of machine learning in various wireless communication scenarios, including the use of Q-learning for femto/small-cell networks and multi-armed bandits for device-to-device networks. The authors conclude that machine learning is a promising area for artificial intelligence aided networking research, and that it has the potential to significantly improve the performance of next-generation wireless networks. They also mention that future research should focus on the development of more efficient machine learning algorithms and their application in various wireless communication scenarios.