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<p><span style="font-family:"Calibri","sans-serif";color:black">Hi Everyone!<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">This week grad student Patrick Gray will be presenting on an area within machine learning called "reinforcement learning." This presentation will include the fundamental ideas and math behind the
topic and its applications to the real world. If you're interested in expanding your knowledge of machine learning, join us this Wednesday (11/9) at 7:30pm in ARC 106!<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">Abstract:<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">"Reinforcement learning is a type of machine learning in which computers automatically learn how to act within a particular environment so as to maximize a numerical reward signal. The computers
are never told which actions to take, but instead must discover for themselves, through trial and error, the most rewarding actions. For example, a self-driving car implemented with a reinforcement learning algorithm will automatically learn how to properly
adjust its steering wheel through a reward system that reinforces stability and deters swerving. Although this idea seems rather simple, it works quite well in practice. All in all, reinforcement learning is an exciting area of artificial intelligence and
will undoubtedly power many of the automated technologies of the near future. <o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">On Wednesday, I will walk you through the basics of reinforcement learning, from the common nomenclature to the fundamental (and not at all daunting) math. To help solidify your understanding of
the basics, I will also present to you a comprehensible solution to a simple, yet important, real-world reinforcement learning problem. The presentation will conclude with a survey of some very impressive reinforcement learning systems that trained themselves
to fly helicopters, diagnose diseases, and even play some classic video games."<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">- Patrick Gray<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">We hope to see you all there!<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">Sincerely,<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">Catherine Baugher<o:p></o:p></span></p>
<p><span style="font-family:"Calibri","sans-serif";color:black">OU ACM Secretary<o:p></o:p></span></p>
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