Single-layer models built from first principles: linear regression, vectorized loss, partial derivatives, the chain rule, gradient descent, and nonlinear feature maps.

A structured deep-neural-network review spanning initialization, optimization, CNNs, sequence models, attention, Transformers, and modern representation learning.

Three downloadable CS170 final-review cheat sheets collected in one place for compact, exam-focused revision.

Two downloadable CS188 final-review cheat sheets for quickly revisiting the course’s core artificial-intelligence methods.

A growing set of abstract algebra notes covering modular arithmetic, groups, homomorphisms, subgroups, symmetric groups, and the proof techniques behind them.

Notes on 2D projective geometry: homogeneous coordinates, conics, transformations, cross-ratios, points at infinity, and affine or metric rectification.

A broad machine-learning review covering Bayesian decisions, estimation, linear classifiers, neural networks, SVMs, dimensionality reduction, clustering, and ensembles.

A technical reading of the five-point minimal solver for event-camera relative motion, from Plücker coordinates and incidence geometry to the final polynomial solution.