This course focuses on machine learning models and their deployments. Topics include neural networks (principles, optimization, generalization) recent neural network models (convolutional, self-attention, transformers, generative adversarial networks) and system issues in machine learning(on-device machine learning federated learning).

Examination: Questions are designed to see how well the students have learned the basic concepts, fundamental theory, and applications of learning algorithms.