10-601 Logistic Regression

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



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.