This lecture takes the learning flow diagram from Lecture 1 and swaps one part at a time along four directions: the output space Y (binary classification, multiclass classification, regression, structured learning), the labels yₙ (supervised, unsupervised, semi-supervised, reinforcement learning), the way data reaches the algorithm (batch, online, active), and the input space X (concrete, raw, abstract features). Each direction has one core option, and together they mark the home ground for the rest of Foundations: batch, supervised binary classification and regression on concrete features.
The opening lecture of CS229 Spring 2026 runs only 37 minutes and has no derivations. It defines machine learning with Tom Mitchell's experience E, task T, and performance measure P, then splits the whole course into supervised, unsupervised, and reinforcement learning. The three differ in whether the data comes with answers and whether the model collected the data itself. Large language models show up in all three categories at once.
Lecture 2 breaks supervised learning into three pieces: a hypothesis h, the least-squares cost J(θ), and an algorithm that drives J down. Gradient descent sweeps all n examples on every step; minibatch SGD updates after looking at a small batch, and pays for it by bouncing around near the optimum. Linear regression also has a closed-form solution, θ = (XᵀX)⁻¹Xᵀy, provided XᵀX is invertible.
Week 4 covers three forms of machine learning. Supervised learning fits a hypothesis function to labeled data: classify by the k nearest neighbors, or draw a decision boundary with a set of weights (perceptron learning rule, logistic regression, support vector machines), then use loss functions, regularization and cross-validation against overfitting. Reinforcement learning uses Q-learning to update the estimated value of each state and action from rewards, with ε-greedy keeping some probability of random exploration. Unsupervised learning clusters with k-means. With time links for every section, plus the approach and ways to check your work for the Shopping and Nim projects.
Lectures 1–12 form one decision pipeline: define states, moves, and objectives, then use heuristics, losses, regularization, and backpropagation to control an otherwise intractable search space.