Learn about machine learning model adaptability and representation learning in this technical lecture from Prof. Sanjay Shakkottai of The University of Texas at Austin. Explore how models can be effectively trained using data from multiple clients/environments for deployment in new, unseen environments. Dive into two key approaches: Model Adaptive Meta Learning (MAML) and federated learning with FedAvg, examining their theoretical foundations in multi-task linear representation settings. Understand how the bi-level update structure in both approaches leverages client data diversity to achieve optimal representation learning. Follow along as the lecture demonstrates exponential convergence to ground-truth representation and discusses practical applications in wireless communication networks and online platforms. Gain insights into fine-tuning strategies, regularization techniques, model drift considerations, and the fundamental goals of adaptive learning models.