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<!DOCTYPE html>
<html lang="en" prefix="og: http://ogp.me/ns#">
<head>
<title>Machine Learning Advanced Probablistic Methods</title>
<link href="images/icon.svg" rel="icon">
<link href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,700%7CRoboto+Slab:700%7CRoboto:400,400i,700" rel="stylesheet"><link href="ppc.css" rel="stylesheet" type="text/css">
<meta charset="UTF-8">
<meta content="width=device-width, initial-scale=1" name="viewport">
<meta content="https://fartaha.github.io/" property="og:url">
<meta content="images/icon.svg" property="og:image">
<meta content="Machine Learning Advanced Probablistic Methods" property="og:title">
<meta content="CS-E4820" property="og:description">
<meta content="article" property="og:type">
<meta content="summary" name="twitter:card">
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# Make a slideshow #
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<style>
* {box-sizing: border-box}
.mySlides {display: none}
img {vertical-align: middle;}
/* Slideshow container */
.slideshow-container {
max-width: 1000px;
position: relative;
margin: auto;
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.next {
right: 0;
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</style>
</head>
<body>
<div id="wrap">
<div id="heading">
<div class="menubar menufirst">
<span class="menugroup"><a href="." class="menubutton selected" title="Deep Learning with PyTorch">CS-E4820</a></span><span class="menugroup"><a href="." class="menubutton" title="Exercises">Assignments</a></span>
</div>
<div id="titleblock">
<h1>ML Advanced Probabilistic Methods</h1>
</div>
<div class="menubar menusecond">
<span class="menugroup">
<a href="." class="menubutton selected" title="Introduction">Lec1</a>
</span>
<span class="menugroup">
<a href="
/ml-advanced-probabilistic-methods/lectures/lec2.html" class="menubutton" title="Chapter 1: Role of parallelism">Lec2</a>
</span>
<span class="menugroup">
<a href="lec3/" class="menubutton" title="Lectures">Lec3</a>
</span>
</div>
<center>
<figure>
<img src="images/cover1.png" style="width:50%">
<p style="font-size: 8px">Image from 1RT003 Course at Uppsala University <a href="https://uppsala.instructure.com/courses/53069/pages/lectures">(link)</a></p>
</figure>
</center>
<div id="maintitle"><h1>Introduction</h1></div>
</div>
<div id="body">
<p align="justify">This is a repo to summarize a good course I have taken this semester at Aalto University. The course code is CS-E4820, lectured by <a href="https://scholar.google.com/citations?hl=en&user=id47-5cAAAAJ&view_op=list_works&sortby=pubdate">Prof. Pekka Martinen.</a> I will update the page after each in-person session of the course summarising the most important parts of the lectures as well the examples solved in session. I also add some extra examples from other sources as well when it is necessary.</p>
<p align="justify">Feel free to make a pull request <a href="https://github.com/fartaha/ml-advanced-probabilistic-methods">@MLAPM</a>. If you are not familiar with HTML WEB pages, just edit the README.md to add your notes there and I will take care of the html things. Remember to create a pull request when your editing is done.</p>
<p align="justify"><em>Acknowledgements:</em> It is worth mentioning that the CSS file for creating this repo is exactly extracted from the great <a href="https://ppc.cs.aalto.fi/">PPC</a> course @ Aalto University and I have used their website as a template for this material. The only difference however is just modifying the CSS file a little bit to add a slideshow functionality to it.</p>
<ul>
<li>The examples used in this review are mainly extracted from lecture slides and the reference books:
</li>
<ul><li>Bayesian Reasoning and Machine Learning available at <a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online">link</a></li>
<li>Pattern Recognition and Machine Learning available at
<a href="https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/">link</a></li></ul>
<center><img src="images/ref.png" style="width:80%"></center>
</ul>
<h3><span style="background: #FFFFE0">Review</span></h3>
<div class="example">
<h3>1. Ingredients of probabilistic modeling:</h3>
<ul><li><b>Models:</b> <br />Bayesian networks, Sparse Bayesian linear regression,
Gaussian mixture models, latent linear models</li>
<li><b>Methods for inference:</b> <br />maximum likelihood, maximum a posteriori
(MAP), Laplace approximation, expectation maximization (EM),
Variational Bayes (VB), Stochastic variational inference (SVI) <br />
<div class="example">
<strong>Note: </strong><br />We don't go deep in <strong>MCMC methods</strong> because of overlapping with BDA course. The course focuses more on <span style="background: #FFFFE0"><em>deterministic</em></span> inference methods.
</div>
</li>
<li><b>Ways to select between models</b></li>
<li><b>Marginalization:</b><br />
getting rid of one of the variables in joint probablity distribution with summation over the other parameter</li>
<center><img src="images/ex1.png" style="width:80%"></center>
<li><b>Independence:</b><br />
p(b)p(m)=p(b, m)</li>
<center><img src="images/ex2.png" style="width:50%"></center>
</ul>
</div>
<h3>Examples from the slides</h3>
<div class="slideshow-container">
<div class="mySlides fade">
<div class="numbertext"> </div>
<center><img src="images/slide1.png" style="width:75%"></center>
<div class="text"> </div>
</div>
<div class="mySlides fade">
<div class="numbertext"> </div>
<center><img src="images/slide2.png" style="width:75%"></center>
<div class="text"> </div>
</div>
<div class="mySlides fade">
<div class="numbertext"> </div>
<center><img src="images/slide3.png" style="width:75%"></center>
<div class="text"> </div>
</div>
<a class="prev" onclick="plusSlides(-1)">⤺</a>
<a class="next" onclick="plusSlides(1)">⤻</a>
</div>
<br>
<div style="text-align:center">
<span class="dot" onclick="currentSlide(1)"></span>
<span class="dot" onclick="currentSlide(2)"></span>
<span class="dot" onclick="currentSlide(3)"></span>
</div>
<h3>Flag Counter</h3>
<a href="https://info.flagcounter.com/m4fC"><img src="https://s01.flagcounter.com/count2/m4fC/bg_FFFFFF/txt_000000/border_CCCCCC/columns_2/maxflags_10/viewers_0/labels_0/pageviews_0/flags_0/percent_0/" alt="Flag Counter" border="0"></a>
</div>
<div id="footer"></div>
<!--
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# Make a slideshow with JS
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https://css-tricks.com/on-adding-ids-to-headers/
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<script>
let slideIndex = 1;
showSlides(slideIndex);
function plusSlides(n) {
showSlides(slideIndex += n);
}
function currentSlide(n) {
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}
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let dots = document.getElementsByClassName("dot");
if (n > slides.length) {slideIndex = 1}
if (n < 1) {slideIndex = slides.length}
for (i = 0; i < slides.length; i++) {
slides[i].style.display = "none";
}
for (i = 0; i < dots.length; i++) {
dots[i].className = dots[i].className.replace(" active", "");
}
slides[slideIndex-1].style.display = "block";
dots[slideIndex-1].className += " active";
}
</script>
</body>
</html>