Applied Neural Networks

Catalog description

Neural networks form the backbone of modern artificial intelligence methodologies. This course will survey various neural networks architectures with a heavy emphasis on practical application to their specific data use cases. Students will explore how neural networks generalize classical statistical models and function estimation techniques, and how statistical principles inform model design, optimization, and evaluation. Topics include feedforward architectures, stochastic gradient descent, regularization and model selection, convolutional and recurrent networks, and an introduction to attention-based models.