This course, "Machine Learning Paradigms for Next-Generation Wireless Networks," is designed to provide an in-depth understanding of how machine learning can revolutionize the wireless communication landscape, particularly in the context of 5G networks. Key topics include:

• Introduction to the fundamental concepts of machine learning and their applications in wireless networks.
• Exploration of supervised learning techniques such as regression models, K-nearest neighbors (KNN), and support vector machines (SVM), and their use in MIMO channel estimation and energy learning.
• Analysis of unsupervised learning methods, including K-means clustering and principal component analysis (PCA), with applications in network optimization and anomaly detection.
• Examination of reinforcement learning frameworks, such as Q-learning and Markov decision processes (MDP), for adaptive decision-making in dynamic network environments.
• Case studies on the application of machine learning in cognitive radio networks, smart grids, and device-to-device (D2D) communications.

By the end of the course, students will be equipped with the knowledge to apply advanced machine-learning techniques to solve complex problems in next-generation wireless networks.