Curriculum / DescriptionsMSAI 437: Deep Learning
Curriculum
/ Descriptions
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Prerequisites
Core MSAI courseDescription
We will study deep learning architectures: perceptrons, multi-layer perceptrons, convolutional neural networks, recurrent neural networks (LSTMs, GRUs), adversarial networks (GANs), attention networks such as transformers, and the combination of reinforcement learning with deep learning. Other topics discussed will cover recent papers and architectures such as self-supervised learning, diffusion models etc. Learning will be in the practical context of implementing networks using these architectures in a modern programming environment: PyTorch, and will comprise hands-on coding assignments, reading assignments, and a final project where students apply deep learning to a problem domain of their choice.
