<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MAP on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/map/</link><description>Recent content in MAP on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://arshadhs.github.io/tags/map/index.xml" rel="self" type="application/rss+xml"/><item><title>Bayesian Learning</title><link>https://arshadhs.github.io/docs/ai/machine-learning/08-bayesian-learning/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/machine-learning/08-bayesian-learning/</guid><description>&lt;h1 id="bayesian-learning">
 Bayesian Learning
 
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&lt;p>Bayesian Learning is a probabilistic approach to machine learning.&lt;/p>
&lt;p>Instead of only asking, “Which output should the model predict?”, Bayesian Learning asks:&lt;/p>

&lt;blockquote class='book-hint '>
 &lt;p>Given the data we have observed, how likely is each hypothesis, class, or parameter value?&lt;/p>
&lt;/blockquote>&lt;p>This makes Bayesian Learning useful when uncertainty matters.&lt;/p>
&lt;p>It is especially important in classification, probabilistic modelling, generative models, and situations where we want to combine prior knowledge with observed data.&lt;/p></description></item></channel></rss>