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  • Linear Regression
  • Cost Function
  • Optimization
  • Polynomial Regression
  • Logistic Regression

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  1. Machine Learning
  2. ML Notes

Linear Regression

PreviousFeature SelectionNextEntropy, Cross-Entropy, KL Divergence

Last updated 3 years ago

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Linear Regression

See Numerical Method Lecture Note

Cost Function

Optimization

1. Solve analytically:

  • Least square solution using normal equation

  • Don't need to iterate, and no need to design learning rate

  • Slow if the dimension of data (n) is very large.

2. Gradient Descent:

  • can use batch gradient descent

  • Idea: Make sure features are on a similar scale ( e.g ,. −1≤𝑥𝑖≤1): normalization

  • Check the learning rate

  • Need many iterations

  • Works well even when n is large

Derive the Gradient of the cost function J

Polynomial Regression

Logistic Regression

The first thing to say is that logistic regression is not a regression, but a classification learning algorithm.

normal equation