This course will provide an overview of deep learning techniques with engineering applications. Topics covered include: neural network architectures; model training and regularization; data augmentation; transfer learning; generative models; and a brief overview of reinforcement learning. Ethics and fairness will play a prominent role in the course discussions. The course will follow an applied approach through several skill building assignments and a team-based project.