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Starts: Tuesday 20th April 2021
The 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, 25 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.
Early Bird Discount Structure:
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.
Starts: Thursday 3rd June 2021
The objective of the course is to develop fundamental skills of quantitative developer role. The course is of an introductory level and does not require programming experience. The course is designed by practitioners from quantitative finance with experience in model development for derivative pricing and systematic trading. The primary coding languages of the course are Python and C++. As it is essential in finance to work with time series data we introduce database KDB and the language q, which are the leading solutions for storing the timeseries.
The course consists of 5 Modules:
- Python for Finance, C++ fundamentals and use cases from quantitative finance
- Data Structures and Algorithms in C++
- Databases in finance – KDB
- Design of systematic trading platforms
- Throughout the course sample test questions from quant interviews will be provided
Early Bird Discount Structure: