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	<title>blog.muehlburger.at &#187; Machine Learning</title>
	<atom:link href="http://blog.muehlburger.at/category/computer-science/machine-learning/feed/" rel="self" type="application/rss+xml" />
	<link>http://blog.muehlburger.at</link>
	<description>Software Engineering by Herbert Mühlburger</description>
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		<title>Machine Learning is awesome</title>
		<link>http://blog.muehlburger.at/2011/11/16/machine-learning-is-awesome/</link>
		<comments>http://blog.muehlburger.at/2011/11/16/machine-learning-is-awesome/#comments</comments>
		<pubDate>Wed, 16 Nov 2011 11:04:20 +0000</pubDate>
		<dc:creator>Herbert Mühlburger</dc:creator>
				<category><![CDATA[Computer Science]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Artificial intelligence]]></category>
		<category><![CDATA[Contests]]></category>
		<category><![CDATA[Kaggle]]></category>
		<category><![CDATA[Linear Regression]]></category>
		<category><![CDATA[Logistic Regression]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[Tuned IT]]></category>

		<guid isPermaLink="false">http://blog.muehlburger.at/?p=1404</guid>
		<description><![CDATA[Machine Learning Class Machine Learning has always been a topic that attracted my interest. Currently I attend the &#8220;Machine Learning&#8221; class (@ml_class) offered by Prof. Andrew Ng from Stanford University. The class is awesome. The video lectures are made of small chunks explaining every topic in detail. A lot of topics were already covered: Linear [...]]]></description>
				<content:encoded><![CDATA[<h2>Machine Learning Class</h2>
<p>Machine Learning has always been a topic that attracted my interest. Currently I attend the &#8220;<a title="Machine Learning Class" href="http://www.ml-class.org" target="_blank">Machine Learning</a>&#8221; class (<a title="@ml_class" href="http://twitter.com/ml_class" target="_blank">@ml_class</a>) offered by Prof. <a title="Prof. Andrew Ng" href="http://ai.stanford.edu/%7Eang/" target="_blank"><span class="zem_slink">Andrew Ng</span></a> from <a class="zem_slink" title="Stanford University" href="http://en.wikipedia.org/wiki/Stanford_University" rel="wikipedia">Stanford University</a>. The class is awesome. The video lectures are made of small chunks explaining every topic in detail. A lot of topics were already covered:</p>
<ul>
<li><a class="zem_slink" title="Linear regression" href="http://en.wikipedia.org/wiki/Linear_regression" rel="wikipedia">Linear regression</a> with one variable</li>
<li>Linear regression with multiple variable</li>
<li>One-vs-all Classification</li>
<li>Regularization</li>
<li>Backpropagation Algorithm</li>
<li>Neural Networks</li>
<li>Practical advise for applying learning algorithms</li>
<li>How to develop and debug learning algorithms</li>
<li>Feature and model design, setting up experiments</li>
</ul>
<p>Other interesting topics are following. In parallel to the lectures there are homework programming exercises that have to be solved. To date programming assignments covering topics as:</p>
<ul>
<li>Linear regression</li>
<li>Logistic regression</li>
<li>Multi-class classification and Neural Networks</li>
<li>Neural network learning</li>
<li>Regularized linear regression and bias-variance</li>
</ul>
<p>In order to solve the exercises you have to understand the contents and have some programming experience in <a title="GNU Octave" href="http://www.gnu.org/software/octave/" target="_blank">GNU Octave</a>. It is also important to have basic understanding of Linear Algebra.</p>
<h2>Machine Learning Contests</h2>
<p>In order to apply machine learning on real world problems you can enter a machine learning contest. There are various contests out there, some of which are</p>
<ul>
<li><a title="Tuned IT" href="http://tunedit.org/" target="_blank">Tuned IT</a></li>
<li><a title="Kaggle" href="http://www.kaggle.com/" target="_blank">Kaggle</a></li>
</ul>
<p>I will keep you updated.</p>
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		<item>
		<title>[video] &#8211; The Filter Bubble &#8211; From Human to Algorithmic Gatekeepers</title>
		<link>http://blog.muehlburger.at/2011/05/06/video-the-filter-bubble-from-human-to-algorithmic-gatekeepers/</link>
		<comments>http://blog.muehlburger.at/2011/05/06/video-the-filter-bubble-from-human-to-algorithmic-gatekeepers/#comments</comments>
		<pubDate>Fri, 06 May 2011 17:36:10 +0000</pubDate>
		<dc:creator>Herbert Mühlburger</dc:creator>
				<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Computer Science]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Personalization]]></category>
		<category><![CDATA[Eli Pariser]]></category>
		<category><![CDATA[Facebook]]></category>
		<category><![CDATA[Gatekeeper]]></category>
		<category><![CDATA[google]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[new york times]]></category>
		<category><![CDATA[TED]]></category>
		<category><![CDATA[twitter]]></category>

		<guid isPermaLink="false">http://blog.muehlburger.at/?p=1328</guid>
		<description><![CDATA[Web personalization and personalized recommendations are recently gaining more and more interest. Companies like Amazon, Google, Netflix, The New York Times, Facebook, Twitter, &#8230; already personalize their products in different ways. If you take Google&#8217;s search results as an example. Have you ever noticed that a friend of you gets different search results as you [...]]]></description>
				<content:encoded><![CDATA[<p><span class="zem_slink">Web personalization</span> and personalized recommendations are recently gaining  more and more interest. Companies like <a class="zem_slink" title="Amazon" rel="homepage" href="http://amazon.com/">Amazon</a>, <a class="zem_slink" title="Google" rel="homepage" href="http://google.com">Google</a>, <a class="zem_slink" title="Netflix" rel="homepage" href="http://www.netflix.com/">Netflix</a>, <a class="zem_slink" title="New York Times" rel="homepage" href="http://www.newyorktimes.com">The New York Times</a>, <a class="zem_slink" title="Facebook" rel="wikipedia" href="http://en.wikipedia.org/wiki/Facebook">Facebook</a>, <a class="zem_slink" title="Twitter" rel="homepage" href="http://twitter.com">Twitter</a>, &#8230; already personalize their products in different ways. If you take Google&#8217;s search results as an example. Have you ever noticed that a friend of you gets different search results as you do for the same search query? If you never have noticed  just try it out it&#8217;s really worth noting. Another example are Amazon&#8217;s product recommendations which are for example based on your purchases, your product ratings and so on.</p>
<p><a title="Eli Pariser" href="http://elipariser.com/" target="_blank">Eli Pariser</a> explains in the following <a class="zem_slink" title="TED (conference)" rel="wikipedia" href="http://en.wikipedia.org/wiki/TED_%28conference%29">TED Talk</a> how &#8220;human information filters&#8221; get substituted by algorithmic ones, which means how recommendation engines filter information for you. Have a look at the video is is really worth watching:</p>
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<p>Do you know other examples of web personalization or recommendations engines? Please leave me a comment at the end of this post.</p>
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<li class="zemanta-article-ul-li"><a href="http://bigthink.com/ideas/38271">Who Decides What You Read? Hint: It&#8217;s Not You</a> (bigthink.com)</li>
<li class="zemanta-article-ul-li"><a href="http://www.wired.com/epicenter/2011/03/eli-pariser-at-ted/">TED 2011: Junk Food Algorithms and the World They Feed Us</a> (wired.com)</li>
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		<item>
		<title>[video] &#8211; How is Hadoop used at Twitter?</title>
		<link>http://blog.muehlburger.at/2010/09/10/video-how-is-hadoop-used-at-twitter/</link>
		<comments>http://blog.muehlburger.at/2010/09/10/video-how-is-hadoop-used-at-twitter/#comments</comments>
		<pubDate>Fri, 10 Sep 2010 13:02:24 +0000</pubDate>
		<dc:creator>Herbert Mühlburger</dc:creator>
				<category><![CDATA[Business Intelligence]]></category>
		<category><![CDATA[Computer Science]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Videos]]></category>
		<category><![CDATA[Cloudera]]></category>
		<category><![CDATA[Hadoop]]></category>
		<category><![CDATA[Online Communities]]></category>
		<category><![CDATA[Open source]]></category>
		<category><![CDATA[Social Networking]]></category>
		<category><![CDATA[Trending and Popularity]]></category>
		<category><![CDATA[twitter]]></category>
		<category><![CDATA[Yahoo]]></category>

		<guid isPermaLink="false">http://blog.muehlburger.at/?p=1111</guid>
		<description><![CDATA[In the following video Dmitriy Ryaboy, a Twitter Analytics Engineer and a former Cloudera Intern, explains how Twitter uses Hadoop and Pig. Enjoy the video and have a good weekend! Related articles How GE uses Hadoop to analyze big data (news.cnet.com) How GE uses Hadoop to analyze big data (news.cnet.com) Hadoop There it Was&#8211;Hadoop Summit [...]]]></description>
				<content:encoded><![CDATA[<div class="zemanta-img">
<div class="wp-caption aligncenter" style="width: 345px"><a href="http://www.crunchbase.com/product/hadoop"><img title="Image representing Hadoop as depicted in Crunc..." src="http://www.crunchbase.com/assets/images/resized/0001/3073/13073v1-max-450x450.png" alt="Image representing Hadoop as depicted in Crunc..." width="335" height="93" /></a><p class="wp-caption-text">Image via CrunchBase</p></div>
</div>
<p>In the following video Dmitriy Ryaboy, a <a class="zem_slink" title="Twitter" rel="homepage" href="http://twitter.com">Twitter</a> Analytics Engineer and a former <a class="zem_slink" title="Cloudera" rel="homepage" href="http://www.cloudera.com">Cloudera</a> Intern, explains how Twitter uses <a class="zem_slink" title="Hadoop" rel="homepage" href="http://hadoop.apache.org/">Hadoop</a> and <a title="Pig" href="http://hadoop.apache.org/pig/" target="_blank">Pig</a>. Enjoy the video and have a good weekend!</p>
<p><iframe src="http://player.vimeo.com/video/11110059" width="400" height="225" frameborder="0" webkitAllowFullScreen mozallowfullscreen allowFullScreen></iframe></p>
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		</item>
		<item>
		<title>Video by Google on K-Means Clustering</title>
		<link>http://blog.muehlburger.at/2010/08/04/video-by-google-on-k-means-clustering/</link>
		<comments>http://blog.muehlburger.at/2010/08/04/video-by-google-on-k-means-clustering/#comments</comments>
		<pubDate>Wed, 04 Aug 2010 12:43:43 +0000</pubDate>
		<dc:creator>Herbert Mühlburger</dc:creator>
				<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Computer Science]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Cluster analysis]]></category>
		<category><![CDATA[K-means clustering]]></category>

		<guid isPermaLink="false">http://blog.muehlburger.at/?p=1066</guid>
		<description><![CDATA[Recently I was searching for good ressources explaining the k-means clustering algorithm. I found the following video from Google Research on YouTube. It explains the K-Means clustering algorithm quite well. Related articles The Difference between Segmentation and Clustering (zyxo.wordpress.com) Clustergram: visualization and diagnostics for cluster analysis (R code) (r-statistics.com) Clustering large datasets (metalev.org)]]></description>
				<content:encoded><![CDATA[<p>Recently I was searching for good ressources explaining the <a title="k-means clustering algorithm" href="http://en.wikipedia.org/wiki/K-means_clustering" target="_blank">k-means clustering algorithm</a>. I found the following video from Google Research on <a title="YouTube" href="http://www.youtube.com" target="_blank">YouTube</a>. It explains the K-Means clustering algorithm quite well.</p>
<p><iframe width="425" height="344" src="http://www.youtube.com/embed/1ZDybXl212Q?wmode=transparent" frameborder="0" allowfullscreen> </iframe></p>
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