Event Description
Join the Drexel Women in Computing Society (WiCS) for a talk with Electrical and Computer Engineering Associate Professor Andrew Cohen, PhD on the use of Kolmogorov complexity and algorithmic statistics in clustering data.
Most modern learning theory starts with Bayes’ representation of
knowledge from probability. In the 1970s, Andrei Kolmogorov put forth a
different approach to statistics that coined Kolmogorov Complexity and
how we solve the problem of data clustering.
Algorithmic
statistics use the length in bytes of computer programs to measure how
well that program (model, algorithm, etc.) fits your data. Rather than
searching over a probability distribution, you search over a space of
algorithms and settings. In this talk, we will explore the new cluster structure-function
that tells us optimally how many clusters are in the dataset while it
ensures that the differences within/between each cluster are as random
and meaningful as possible.
We will discuss examples and results of such techniques: spatiotemporal human breast organoid patterning, proliferating cell applications, and plagiarism deterrence for beginning programmers.
This free, virtual event is open to all Drexel students.
Register here
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