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