Stanford Machine Learning

taught by Andrew Ng

Syllabus

1. Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
2. Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
3. Logistic regression, One-vs-all classification, Regularization.
4. Neural Networks.
5. Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
6. Support Vector Machines (SVMs) and the intuition behind them.
7. Unsupervised learning: clustering and dimensionality reduction.
8. Anomaly detection.
9. Recommender systems.
10. Large-scale machine learning. An example of an application of machine learning.