Stanford CS229: Machine Learning by Andrew Ng

📌 What is this course?

Taught by Professor Andrew Ng (founder of Google Brain, Coursera, and DeepLearning.AI), Stanford CS229: Machine Learning is the most celebrated collegiate machine learning curriculum in the world.

This course goes far beyond surface-level tooling to teach the rigorous mathematical foundations, statistical learning theory, and algorithmic proofs of machine learning. You will master linear regression, logistic regression, Generalized Linear Models (GLMs), Support Vector Machines, Kernel methods, the EM algorithm, Principal Component Analysis (PCA), and Reinforcement Learning (MDPs and Q-Learning).

Stanford provides all official lecture slides, in-depth mathematical PDF notes, and high-definition YouTube recordings completely free to the public.

---

🎯 Who is this for?

• Students and engineers seeking a deep theoretical and mathematical mastery of machine learning algorithms.
• Researchers and candidates preparing for rigorous machine learning engineering and research scientist technical interviews.
• Anyone who wants to learn directly from Andrew Ng’s iconic blackboard derivations.

---

📚 Core Syllabus

1. Supervised Learning: LMS algorithm, Normal equations, Logistic regression, Newton’s method
2. Generative Learning Algorithms: Gaussian Discriminant Analysis (GDA), Naive Bayes classifier
3. Support Vector Machines: Functional and geometric margins, Lagrange duality, KKT conditions, Kernel trick
4. Learning Theory: Bias/variance tradeoff, Union bounds, VC dimension, Regularization
5. Unsupervised Learning & Clustering: k-Means, Mixture of Gaussians, Expectation-Maximization (EM) algorithm
6. Dimensionality Reduction: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), ICA
7. Reinforcement Learning: Markov Decision Processes (MDP), Bellman equations, Value iteration, Q-Learning