In Class Demos

Notebooks and code demos. You may copy and modify for use in EE 541.

Week 1 — Introduction to Deep Learning

• Fashion-MNIST Dataset — Dataset statistics, class distribution, visualization
• Minimal PyTorch Example — Two-layer network on Fashion-MNIST, training loop
• Feature Visualization — PCA projections before and after training

Week 3 — MMSE Estimation

• MMSE Estimation — Sensor estimation under Gaussian and non-Gaussian noise
• Vector LMMSE — Orthogonality verification, sample convergence, ridge regularization
• LMS Adaptive Filtering — Audio noise cancellation, step-size effects

Week 5 — Classification and Logistic Regression

• Classifier Evaluation — Threshold tuning on ROC and PR curves
• Logistic Regression MLE — 4-point logistic MLE, loss surface, Newton vs GD