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

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Jan 12, 2024 35 likes •151 views

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

SLIDE 1

Deep Learning - Theory and Practice

13-02-2020

Linear Regression, Least Squares Classification and Logistic Regression

http://leap.ee.iisc.ac.in/sriram/teaching/DL20/ deeplearning.cce2020@gmail.com

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Linear Regression

Bishop - PRML book (Chap 3)

❖ Solution to Maximum Likelihood problem is the least

squares solution

Pseudo Inverse Based Solution

SLIDE 3

Choice of Basis Functions

SLIDE 4

Regularized Least Squares

Bishop - PRML book (Chap 3)

❖ Optimize a modified cost function

SLIDE 5

Regularized Least Squares

Bishop - PRML book (Chap 3)

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Choice of Regularization Parameter

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Linear Models for Classification

Bishop - PRML book (Chap 3)

❖ Optimize a modified cost function

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Least Squares for Classification

Bishop - PRML book (Chap 3)

❖ K-class classification problem ❖ With 1-of-K hot encoding, and

least squares regression

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Logistic Regression

Bishop - PRML book (Chap 3)

❖ 2- class logistic regression ❖ K-class logistic regression ❖ Maximum likelihood solution ❖ Maximum likelihood solution

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Least Squares versus Logistic Regression

Bishop - PRML book (Chap 4)

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Least Squares versus Logistic Regression

Bishop - PRML book (Chap 4)