machine learning event

events.xml

@@ -4,6 +4,37 @@
 <eventdefs>
 
 <!-- Spring 2017 -->
+<eventitem date="2017-06-15" time="6:00 pm" room="TBA"
+           title="Understanding machine learning - a theory perspective">
+
+   <short>
+      <p>
+     	Professor Shai Ben-David will discuss the basic principles behind machine learning and how they relate to some of 
+	the headline-making practical tools, in addition to the major rearch challenges and directions that address
+	the fast expanding scope of potential machine learning applications.
+     </p>
+   </short>
+   <abstract>
+      <p>
+	We are all aware that we live in the era of ("big") data. In contrast to classical scientists 
+	that devoted much of their resources to collecting data, nowadays researchers are flooded with
+	data and the focus has switched to trying to make sense of and utilize the big and complex available data.
+	Machine learning is aimed to use computer power to do just that. 
+      </p>
+      <p>
+	It is therefore no wonder that machine learning is currently a hot topic. Evidence is all over the map, from
+	NYTimes articles to being a top priority for research investments by Google, Amazon, Microsoft and Facebook.
+	Throughout its (short) history, machine learning has enjoyed fruitful interactions between theory and practice.
+	The growing awareness to its power keeps stimulating research towards new applications to the field, which in turn
+	spur the development of algorithms and inspire new frontiers for our theoretical pursuit. 
+      </p>
+      <p>	
+	In this talk Professor Shai Ben-David will explain the basic principles behind machine learning and how these principles relate to some
+	of headline-making practical tools. Ben-David will also describe some of the major research challenges and research
+	directions that address the fast expanding scope of potential machine learning applications.
+      </p>
+   </abstract>
+</eventitem>
 <eventitem date="2017-06-01" time="6:00 pm" room="MC 3003"
            title="Unix 102">