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.
- 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.