The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming.

All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course.

Content

• 01 and 02: Introduction, Regression Analysis and Gradient Descent
• 03: Linear Algebra - review
• 04: Linear Regression with Multiple Variables
• 05: Octave[incomplete]
• 06: Logistic Regression
• 07: Regularization
• 08: Neural Networks - Representation
• 09: Neural Networks - Learning
• 10: Advice for applying machine learning techniques
• 11: Machine Learning System Design
• 12: Support Vector Machines
• 13: Clustering
• 14: Dimensionality Reduction
• 15: Anomaly Detection
• 16: Recommender Systems
• 17: Large Scale Machine Learning
• 18: Application Example - Photo OCR
• 19: Course Summary

To access this material, follow this link.