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Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course covers deep learning (DL) methods, healthcare data, and applications using DL methods. The courses include activities such as video lectures, self-guided programming labs, homework assignments (both written and programming), and a large project. You are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks and deep generative learning. You will also get a chance to learn different healthcare applications using DL methods such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. Besides learning DL algorithms, the course will focus on hands-on experiences for data scientists and machine learning engineers to implement various practical healthcare models on diverse medical data. You will learn popular deep learning frameworks