This is a specialized machine learning course that focuses on modern deep learning models and approaches. The major topics include modern convolutional neural networks, modern recurrent neural networks, transformers, graph neural networks, generative adversarial networks, neural architecture search, automated data augmentation, self-supervised learning, contrastive learning, adversarial attacks and defenses, and deep reinforcement learning. Students will also use some of these models to develop applications in the course projects to gain practical experience.