Neural Networks

EEE 443 builds your understanding of deep learning from the ground up — starting from how a single neuron computes, then layering on backprop, recurrence, convolution, and attention until you can reason about why modern architectures like transformers and diffusion models actually work. Most of your grade comes from four projects where you implement and train these networks yourself, so the math from linear algebra and probability stops being abstract and becomes the thing you debug at 2am. It sits as the natural follow-up to a signals/ML-flavored background and is the course that prepares you for research or industry work in computer vision, NLP, and generative modeling.