https://cs229.stanford.edu/

Course Project

• Deliverables: proposal (300-500 words), milestone (3 pages), final report (5 pages), poster
• Evaluation: technical quality, originality, community

Lectures

• SU-CS229 SEP222025

Basics + Linear Methods

• SU-CS229 SEP242025
• SU-CS229 SEP292025
• SU-CS229 OCT012025

Regularization

• SU-CS229 OCT062025
• SU-CS229 OCT082025

Kernel Methods

• SU-CS229 OCT131015
• SU-CS229 OCT152025

Decision Trees and Boosting

• SU-CS229 OCT202025
• SU-CS229 OCT222025

Deep Learning

• SU-CS229 OCT272025

Neural networks are a non-linear learning architecture that involves a combination of matrix multiplication and entry-wise non-linear operations.

two layers

constituents

Consider a two layer neural network with:

• \(m\) hidden units
• \(d\) dimensional input \(x \in \mathbb{R}^{d}\)

requirements

\begin{align} &\forall j \in \qty {1, \dots, m}\\ &z_{j} = w_{j}^{(1)}^{T} x + b_{j}^{(1)}\\ &a_{j} = \text{ReLU}\qty(z_{j}) \\ &a = \qty(a_1, \dots, a_{m})^{T} \in \mathbb{R}^{m} \\ &h_{\theta} \qty(x) = w^{(2)}^{T} a + b^{(2)} \end{align}

gradient descent makes a pass over all points to make one gradient step. We can instead approximate gradients on a minibatch of data. This is the idea behind stochastic-gradient-descent.

\begin{equation} \theta^{t+1} = \theta^{t} - \eta \nabla_{\theta} L(f_{\theta}(x), y) \end{equation}

this terminates when theta differences becomes small, or when progress halts: like when \(\theta\) begins going up instead.

we update the weights in SGD by taking a single random sample and moving weights to that direction.

strongly connected components expose local communities in a graph.

constituents

graph \(V,E\)

requirements

strongly connected: for all \(v,w \in V\), there is a path from \(v \to w\), and there’s a path for \(w \to v\).

We can decompose a graph into strongly connected components where a subgraph is strongly connected. (i.e. they form equivalence class under “is strongly connected.”)

additional information

Kosaraju’s Algorithm

A way to find strongly connected components in linear time \(O\qty(n+m)\).