Deep Learning - Theory and Practice Linear Regression, Least Squares - PowerPoint PPT Presentation

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Deep Learning - Theory and Practice Linear Regression, Least Squares 13-02-2020 Classification and Logistic Regression http://leap.ee.iisc.ac.in/sriram/teaching/DL20/ deeplearning.cce2020@gmail.com Linear Regression Solution to Maximum

Deep Learning - Theory and Practice Linear Regression, Least Squares 13-02-2020 Classification and Logistic Regression http://leap.ee.iisc.ac.in/sriram/teaching/DL20/ deeplearning.cce2020@gmail.com
Linear Regression ❖ Solution to Maximum Likelihood problem is the least squares solution Pseudo Inverse Based Solution Bishop - PRML book (Chap 3)
Choice of Basis Functions
Regularized Least Squares ❖ Optimize a modified cost function Bishop - PRML book (Chap 3)
Regularized Least Squares Bishop - PRML book (Chap 3)
Choice of Regularization Parameter
Linear Models for Classification ❖ Optimize a modified cost function Bishop - PRML book (Chap 3)
Least Squares for Classification ❖ K-class classification problem ❖ With 1-of-K hot encoding, and least squares regression Bishop - PRML book (Chap 3)
Logistic Regression ❖ 2- class logistic regression ❖ Maximum likelihood solution ❖ K-class logistic regression ❖ Maximum likelihood solution Bishop - PRML book (Chap 3)
Least Squares versus Logistic Regression Bishop - PRML book (Chap 4)
Least Squares versus Logistic Regression Bishop - PRML book (Chap 4)

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