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Lecture Notes by Andrew Ng : Full Set

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

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