In this training, you will develop a basic understanding of quantum computing and how it can be used in machine learning models, with special emphasis on generative models. We will focus on a particular architecture, the quantum circuit Born machine (QCBM), and use it to generate a simple dataset of bars and stripes.
What To Do When AI Fails:
AI Incident Response
This talk will outline a new approach to “incident response” specifically tailored to AI and it will present a free and open sample AI incident response plan. Participants will leave understanding when and why AI creates liability for the organizations that employ it, and how organizations should react when their AI causes major incidents.
Explainability of Supervised Machine Learning
Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. Join Nadia Burkart and Dr. Marco Huber in a discussion on Explainability of Supervised Learning.
Hilbert Space Kernel Methods for Machine Learning:
Daniel will, in the first part of this talk, overviews RKHS (Reproducing Kernel Hilbert Space) methods and some of their applications to statistics and machine learning. Jean-Marc will then present and discuss a Python library called codpy (curse of dimensionality - for Python), that is an application oriented library supporting Support Vector Machine (SVM) and implementing RKHS methods, providing tools for machine learning, statistical learning and numerical simulations.
for Fairly and Transparently
Expanding Acces to Credit
Join QuantUniversity for a complimentary fall speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.