<div dir="ltr"><pre style="color:rgb(0,0,0)">Sayın Liste Üyeleri,
Aralık ayının seçkin semineri 6 Aralık 2023 Çarşamba günü saat 18.00'de. Uygulamalı Matematiğin liderlerinden Ingrid Daubechies, çok ilginç bir konuşmayla karşımızda. </pre><pre style="color:rgb(0,0,0)">Zoom link: <a href="https://nyu.zoom.us/j/96571913368?pwd=YStxRjJ3U3JxdmN5Qko0N3JHeDdFZz09" target="_blank" style="font-family:Arial,Helvetica,sans-serif">https://nyu.zoom.us/j/96571913368?pwd=YStxRjJ3U3JxdmN5Qko0N3JHeDdFZz09</a></pre><pre style="color:rgb(0,0,0)">Konuşmacı: Ingrid Daubechies (Duke University) </pre><pre style="color:rgb(0,0,0)"><span style="font-family:Arial,Helvetica,sans-serif">Başlık: Old-fashioned Machine Learning: Using Diffusion Methods to Learn Underlying Structure</span><br></pre><pre style="color:rgb(0,0,0)">Özet: Many datasets consist of complex items that can be reasonably surmised to lie on a manifold of much lower dimension than the number of
parameters or coordinates with which the individual items are acquired.
Manifold diffusion is an established method, used successfully to parametrize such datasets much more succinctly. The talk describes an enhancement of this method: when each individual item is itself a complex object, as is the case in many applications, one can model the
collection as a fiber bundle, and build a fiber bundle diffusion operator from which one can gradually learn properties of the underlying
base manifold. This will be illustrated with applications to morphological evolutionary studies in biology.
Poster: <a href="https://tmd.org.tr/wp-content/uploads/2023/09/ColloquiumARALIK-2023poster_page-0001-1.jpg">https://tmd.org.tr/wp-content/uploads/2023/09/ColloquiumARALIK-2023poster_page-0001-1.jpg</a>
<a href="https://tmd.org.tr/tms-distinguished-colloquium-series/">https://tmd.org.tr/tms-distinguished-colloquium-series/</a></pre><pre style="color:rgb(0,0,0)"><pre style="text-wrap: wrap;"><a href="https://en.wikipedia.org/wiki/Ingrid_Daubechies">https://en.wikipedia.org/wiki/Ingrid_Daubechies</a></pre><pre style="color:rgb(0,0,0)">Tüm matematikseverleri bekliyoruz. Konuşmayı bölümlerinizde paylaşabilirseniz seviniriz.</pre>Saygılarımızla</pre><pre style="color:rgb(0,0,0)">TMD Seçkin Seminerler Komitesi</pre></div>