CS-5814: Introduction to Deep Learning

Description: History and basic concepts of artificial neural networks. Activation functions, optimization methods and regularization strategies used in deep multi-layered networks. Network architectures such as convolutional networks and recurrent neural networks. Deep reinforcement learning algorithms including deep Q•learning and policy gradient methods. Deep unsupervised models such as auto-encoders, and deep generative models including variational auto-encoders and generative adversarial networks. Advanced topics in deep learning such as transformers, graph neural networks, and ethics in Artificial Intelligence (AI). Deep learning(DL) applications such as text analysis, computer vision, and visual question answering. A platform for implementation of DL models will be used, such as Tensorflow or PyTorch.

Pathways: N/A

Course Hours: 3 credits

Corequisites: N/A

Crosslist: N/A

Repeatability: N/A