# [ Lecture Note of Andrew Ng ML ]

# Stanford Machine Learning

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 my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course.

**Andrew Ng**

## Content

01 and 02: Introduction, Regression Analysis and Gradient Descent

04: Linear Regression with Multiple Variables

08: Neural Networks – Representation

09: Neural Networks – Learning

10: Advice for applying machine learning techniques

11: Machine Learning System Design

Ex06: Octave Programming

13: Clustering (Unsupervised Learning Introduction)

Ex07: Octave Programming

Ex08: Octave Programming

17: Large Scale Machine Learning

18: Application Example – Photo OCR