ECE 54850 - Deep Learning Theory And Applications

Prerequisite(s): ECE 31100 FOR LEVEL UG WITH MIN. GRADE OF D- AND ECE 49500 FOR LEVEL UG WITH MIN. GRADE OF D-

Credit Hours: 3.00. This course will cover both the practical and theoretical foundations of deep neural networks (DNN). Key topics include probabilistic foundation of DNN, computation graphs, DNN architectures and optimization methodologies. In this course, we aim to a wide range of deep neural network architectures for different applications, from basic models to advanced ones like auto-encoders, convolutional networks, recurrent neural networks (e.g. transformers) and graph neural networks and large language models. Additionally, this course discusses deep generative models such as, variational auto-encoders, generative adversarial networks. Real-world applications (e.g., image segmentation and classification, image and audio generation) from different fields will be showcased throughout the course. The tutorials will enhance understanding by providing hands-on experience in implementing and using deep neural networks with Keras and PyTorch.

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