Understand the core Python constructs needed to build scalable data science and machine learning applications
Learn how to build pragmatic AI and ML applications with case studies in finance
Address the key model risk management and validation challenges when deploying data science and machine learning models in the enterprise
Learn about the drivers and opportunities in Fintech as we move towards digital automation and online services.
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.
Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
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.
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.
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.
In this talk we present and discuss a python library, named codpy (curse of dimensionality - for python), that consist in an application-oriented implementation of RKHS (Reproducing Kernel Hilbert Space) methods, also called Support Vector Machines. We claim that this approach has much more potential and expressivity than traditional Machine Learning technics, as neural networks-based one. We will explain why, and we will illustrate how, this library outperforms neural networks-based libraries on two kind of important, sometimes critical, applications.
