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🗣️Add meetup talk
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kozodoi committed Jul 13, 2024
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<h1>Talks</h1>
<p><span class="cover-desc" style="color:var(--page-desc-color)">I regularly give talks on ML topics at academic conferences, AI meetups and other events. Below, you can find links to videos and slides of selected talks.</span></p>
<p><span class="cover-desc" style="color:var(--page-desc-color)">I regularly give talks on AI and ML topics at academic conferences, AI meetups and other events. Below, you can find links to videos and slides of selected talks.</span></p>
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<p><span class="page-desc">I regularly give talks on ML topics at academic conferences, AI meetups and other events. Below, you can find links to videos and slides of selected talks.</span></p>
<p><span class="page-desc">I regularly give talks on AI and ML topics at academic conferences, AI meetups and other events. Below, you can find links to videos and slides of selected talks.</span></p>


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## Meetups

<ul>
<li>
<b>AI for Credit Risk Analytics</b>
<dd><bb>GuildData, 2024 (Berlin)</bb></dd>
</li>
</ul>
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<span id="dots11"></span>
<li><span id="abs11" style="display: none;"><p></p><b>Abstract:</b> In financial institutions, ML-based credit scorecards are only trained over the labeled data of previously accepted applicants, whose repayment behavior has been observed. This creates sampling bias: the training data represent a limited region of the distribution on which the model is deployed for screening new customers. In this talk, I will illustrate the adverse impact of sampling bias on training and evaluation of scoring models. I will also overview possible methods to address this problem.</span></li>
<li>
<button class="btn" onclick="show_abstract(dots_id = 'dots11', abs_id = 'abs11', btn_id = 'b11')" id="b10">&#128220; Abstract</button>
<button class="btn" onclick="window.open('https://kozodoi.me/talks/GD_2024.pdf')" type="button">&#128202; Slides</button>
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<p></p>

<ul>
<li>
<b>Building Chatbots That Know All About Your Business: Retrieval Augmented Generation</b>
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