Quantitative finance is moving into a new era. Traditional quant skills are no longer adequate to deal with the latest challenges in finance. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight.
The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit down examination using our global network of examination centres.
This course has been designed to empower individuals who work in or are seeking a career in machine learning in finance. Throughout our unique MLI programme, candidates work with hands-on assignments designed to illustrate the algorithms studied and to experience first-hand the practical challenges involved in the design and successful implementation of machine learning models. The MLI is a career-enhancing professional qualification, that can be taken worldwide.
Yields.io is the first FinTech platform that uses AI for real-time model risk management on an enterprise-wide scale.
Our clients use our solution to speed up model validation tasks, to generate regulatory compliant documentation and to industrialize model monitoring. The platform works with all models that are used within the financial sector such as credit risk models, valuation algorithms, market risk, AML, AI and behavioural models.
Yields.io was founded by Jos Gheerardyn and Sébastien Viguié. The company is expanding quickly and has offices in Brussels and London. Yields.io has an international portfolio of clients with both investment banks as well as regional financial institutions.
Graphcore has created a completely new processor, the Intelligence Processing Unit (IPU), specifically designed for machine intelligence. The IPU’s unique architecture means developers can run current machine learning models orders of magnitude faster. More importantly, it lets AI researchers undertake entirely new types of work, not possible using current technologies, to drive the next great breakthroughs in general machine intelligence.
We believe our IPU technology will become the worldwide standard for machine intelligence compute. The performance of Graphcore’s IPU is going to be transformative across all industries and sectors, whether you are a medical researcher, roboticist or autonomous car manufacturer.
The Numerical Algorithms Group (NAG) are experts in numerical algorithms, software engineering and high-performance computing. They have served the finance industry with numerical software and consulting services for over four decades because of their outstanding product quality and technical support. Specifically, relevant to the finance industry, NAG pioneer in the provision of the NAG Library – numerical, machine learning and statistical components ideal for building Quant Libraries, Risk Applications and the like.
NAG also provides best-in-class C++ operator-overloading AD tools for CPU and GPU called dco (derivative computation through overloading) and dco/map (dco meta adjoint programming). The NAG Library and AD tools are used by many of the largest Investment Banks where they are embedded in Quant Libraries and XVA applications. As a not-for-profit company, NAG reinvests surpluses into the research and development of its products, services, staff and its collaborations. www.nag.com
Both through regulation and industry practice, there is an increasing number of risk calculations that need to be done on a regular basis. These calculations require the valuation of portfolios on up to hundredths of thousands of scenarios making them computationally very expensive in time and cost.
MoCaX technology, based on Chebyshev Spectral Decomposition methods, is a methodology and software application which massively reduces the computational burden in a risk calculation. This is achieved by pricing the portfolio on very small number of pre-defined collection of points yielding an object capable of approximating a pricing function and its greeks to a very high degree of accuracy. The object can then be evaluated on thousands of risk scenarios in an ultra-efficient and numerically stable manner.
Several benefits are obtained with this technology. Applications include Market Risk VaR, IMA-FRTB, Dynamic Initial Margin for MVA and IMM, Exposure profiles for CVA and IMM, what-if analysis tools, etc.