CS 229: Machine Learning

Stanford course with 17 lecture notes.

Lecture Timeline

• 2015-10-13 — SU-CS229 OCT132025
• 2024-01-06 — SU-CS229 JAN062025
• 2025-01-08 — SU-CS229 JAN082025
• 2025-09-22 — SU-CS229 SEP222025
• 2025-09-24 — SU-CS229 SEP242025
• 2025-09-29 — SU-CS229 SEP292025
• 2025-10-01 — SU-CS229 OCT012025
• 2025-10-06 — SU-CS229 OCT062025
• 2025-10-07 — SU-CS229 OCT272025
• 2025-10-08 — SU-CS229 OCT082025
• 2025-10-15 — SU-CS229 OCT152025
• 2025-10-20 — SU-CS229 OCT202025
• 2025-10-22 — SU-CS229 OCT222025
• 2025-10-29 — SU-CS229 OCT292025
• 2025-11-06 — SU-CS229 NOV062025
• 2025-11-10 — SU-CS229 NOV102025
• 2025-11-12 — SU-CS229 NOV122025

Key Topics Referenced

• Bayes Theorem
• Bias Variance Tradeoff
• Boosting
• Continuous State Mdp
• Cost Function
• Decision Tree
• Expectation Maximization
• Exponential Family
• Gaussian Mixture Model
• Generalized Linear Models
• Generative Learning Algorithm
• Gradient Descent
• House Price Prediction
• K Means Clustering
• Kernel Trick
• Language Model
• Least Squares Error
• Linear Predictor
• Linear Regression
• Logistic Regression
• Machine Learning
• Machine Learning Evaluation
• Markov Decision Process
• Model Evaluation
• Model Selection
• Naive Bayes
• Neural Network
• Newton S Method
• Nonlinear Linear Regression
• Normal Equation