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CS229: Machine Learning [Stanford] - mklabgr - 06-16-2026 CS229: Machine Learning Summary The Stanford course CS229: Machine Learning is a graduate-level introduction to machine learning and statistical pattern recognition that covers both theoretical foundations and practical algorithms. It includes supervised learning methods such as linear and logistic regression, neural networks, support vector machines, and probabilistic models, as well as unsupervised learning techniques like clustering, dimensionality reduction (PCA), and kernel methods.  The course also explores learning theory concepts such as bias–variance tradeoffs, generalization, and sample complexity, along with reinforcement learning and Markov decision processes. In addition to core algorithms, it discusses real-world applications in areas like computer vision, speech recognition, natural language processing, robotics, and data mining. Overall, CS229 aims to give students a rigorous mathematical understanding of how machine learning methods work and how they are applied in practice. COURSE PAGE