Minimizing Expected Loss Machine Learning

This lecture explains the

This is the third and final part of the series Formalizing the

Applying decision theory to supervised

What drives most modern

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Download the AI Foundation model ebook to learn more → https://ibm.biz/BdGsJd Learn more about the

We consider how to make optimal decisions when different types of errors have different costs. We introduce the notion of the

This video is part of the Supervised

This video explains the most widely used principle of

Questions page: https://www.kindsonthegenius.com/2019/05/21/

(ML 3.2) Minimizing conditional expected loss-NC_cTB1PHyQ.mkv

Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote10.html.

Gradient descent is an algorithm used to train

Minimizing

This video is part of the Supervised

This video is part of the Supervised

In this video we take a look at the empirical risk

Loss

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The goal of