Stanford CS229 Machine Learning¶

Notebooks covering the Stanford CS229 ML theory and algorithms course.

Source PDF: cs229.pdf

Notebooks¶

#	Notebook	Topics
01	Linear Regression	Normal equations, least squares, feature scaling
02	Gradient Descent	Batch, stochastic, mini-batch GD, convergence
03	Locally Weighted Regression	Non-parametric regression, bandwidth selection
04	Logistic Regression	Binary classification, sigmoid, cross-entropy
05	Generative Models	Gaussian discriminant analysis, Naive Bayes
06	Support Vector Machines	Margin maximization, kernel trick, SMO
07	Regularization	L1/L2 penalties, bias-variance tradeoff
08	Learning Theory	PAC learning, VC dimension, generalization bounds
09	Decision Trees	CART, pruning, information gain, Gini impurity
10	Neural Networks Basics	Perceptron, feedforward nets, activation functions
11	Neural Networks Advanced	Backprop, dropout, batch norm, architectures
12	ML Strategy	Error analysis, dataset splits, debugging ML
13	Clustering	K-Means, hierarchical, DBSCAN, evaluation
14	Dimensionality Reduction	PCA, t-SNE, autoencoders
15	Reinforcement Learning	MDPs, Q-learning, policy gradient
X01	Anomaly Detection	Gaussian-based, isolation forest
X02	Recommender Systems	Collaborative filtering, matrix factorization
—	Practice Problems	Review exercises across all topics

Prerequisites¶

foundational/ notebooks 01-04 (linear algebra, calculus, probability, gradient descent)
Python 3.8+, NumPy, Matplotlib, scikit-learn

Suggested Order¶

Follow the numbered sequence (01-15). X01-X02 are supplementary.