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07 Logistic Regression and Gradient Descend

Material:

Section about "Logistic Regression" in Ch 4, pp. 136 - 146

Session material

Topics

This lecture will cover the logistic regression algorithm

After attending this lecture and reading the corresponding part of the book, I expect you to be able to:

  • Train a logistic regression (LR) model on a dataset.
  • Understand the difference between linear regression and logistic regression.
  • Understand the concept of maximum likelihood estimation.
  • Explain the key ideas behind logistic regression, and implement a logistic regression classifier in python.
  • Explain and use L1 and L2 regularization in the context of logistic regression, and discuss the difference between these approaches, as well as the importance of the hyperparameter C.
  • Discuss advantages and disadvantages of logistic regression.