Difference between revisions of "10-601 Logistic Regression"

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=== Slides ===
 
=== Slides ===
* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx in Powerpoint]
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* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx in Powerpoint] [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pdf in PDF]
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=== Readings ===
 
=== Readings ===
  
 
* Optional:
 
* Optional:
 
** Murphy 8.1-3, 8.6
 
** Murphy 8.1-3, 8.6
** [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD (for 10605)]
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** [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD] sec 1-3
 
** [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD]
 
** [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD]
  

Latest revision as of 02:37, 18 February 2016

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

Readings

What You Should Know Afterward

  • How to implement logistic regression.
  • How to determine the best parameters for logistic regression models
  • Why regularization matters for logistic regression.
  • How logistic regression and naive Bayes are similar and different.
  • The difference between a discriminative and a generative classifier.
  • What "overfitting" is, and why optimizing performance on a training set does not necessarily lead to good performance on a test set.