AI for Healthcare System Engineering

3.0 credits · Graduate

The purpose of this class is to teach students the fundamentals of artificial intelligence (AI) technologies and how they can be applied in various healthcare system engineering problems. We will introduce conventional AI technologies including supervised learning for tasks like clinical risk prediction and computer assisted diagnosis, unsupervised learning methods for subtype identification and pattern discovery; as well as deep learning methods, including the basic perceptron and feedforward neural networks for standard vectorized data, convolutional neural networks for analyzing medical images, recurrent neural networks and transformer for analyzing event sequences and temporal signals, and graph neural networks for analyzing networks and relational data. The class includes both lectures introducing algorithms and theories, and programming exercises to get hands-on experience on implementing these algorithms with Python.