Lecture Note of Andrew Ng ML

[ 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


01 and 02: Introduction, Regression Analysis and Gradient Descent

03: Linear Algebra – review

04: Linear Regression with Multiple Variables

       Ex01: Octave Programming

05: Octave Tutorial

06: Logistic Regression

       Ex02: Octave Programming

07: Regularization

       Ex05: Octave Programming

08: Neural Networks – Representation

       Ex03: Octave Programming

09: Neural Networks – Learning

       Ex04: Octave Programming

10: Advice for applying machine learning techniques

11: Machine Learning System Design

12: Support Vector Machines

      Ex06: Octave Programming

13: Clustering (Unsupervised Learning Introduction)

       Ex07: Octave Programming

14: Dimensionality Reduction

15: Anomaly Detection

16: Recommender Systems

       Ex08: Octave Programming

17: Large Scale Machine Learning

18: Application Example – Photo OCR

19: Course Summary




Author: iotmaker

I am interested in IoT, robot, figures & leadership. Also, I have spent almost every day of the past 15 years making robots or electronic inventions or computer programs.

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