The Fundamentals of Machine Learning in Finance
Explore the fundamentals of machine learning through this practitioner-led course covering the principal aspects and applications of machine learning.
This introductory course has been developed for delegates new to the subject, or wanting to refresh their knowledge of the fundamental principles of machine learning and how it applies to financial organisations.
This two-day training course will explore the core components of machine learning from objective function to model interpretation and validation.
Attendees will have the opportunity to learn about the importance of data quality and how to identify, process and analyse data appropriately.
Sessions will address the parameters of model selection and explainability and how machine learning can then be applied to financial risk, investment management and portfolio strategies.
Due to the escalation of the COVID-19 developments and the restrictions being placed on travel, Risk Training has taken the decision to provide our May, June and July training courses virtually.
The decision to move remotely has not been taken lightly, but our utmost priority is to safeguard the wellbeing of all our delegates, speakers and staff.
We are hopeful that we will be able to return to our in-person events later this year, however as this unprecedented situation is changing every day, we remain watchful but also focused on delivering this much anticipated course.
Understand the difference between modern statistics, ML and AI
Why machine learning is becoming more important to financial organisations
The importance of identifying data sources and types
Understand the principles of model and variable selection
The differences between supervised and unsupervised learning
Approaches to satisfy explainability
How to identify and monitor risk in ML
Introduction to machine learning
Objective function in ML
From modern statistics to machine learning models
Interpreting and validating a model
Machine learning in investment management and portfolio optimisation
ML for risk practitioners
Machine learning in finance: putting it into practice
Dr Richard Saldanha
Founder & Co-Head
Dr Richard Saldanha is Founder and Co-Head of Oxquant, a consultancy firm that provides expertise and advice on risk management, investments and the impact of artificial intelligence. He is also an Independent Adviser to Oxford Portfolio Advisers.
Richard has over 20 years’ experience in asset management and investment banking in the areas of risk, trading and investments. In particular, he was Global Head of Risk at Investec Asset Management (2010–15), headed a quantitative global macro initiative for the same firm (2006–09) and founded and ran his own hedge fund Oxquant Capital Partners (2004–06).
In addition to his consulting and advisory activities, Richard lectures on statistical machine learning and its applications in finance at Queen Mary University of London. He attended Oriel College, University of Oxford, and holds a doctorate (DPhil) in statistics.
Dr Drago Indjic
Dr Drago Indjic is an expert data science practitioner and is co-head of Oxquant. He is co-founder of Soft-Finance (Geneva), Technology Partnership (Belgrade) and Richfox Capital (Amsterdam). In addition, Drago has been a technology project funding evaluator for the European Union's ambitious €80bn Horizon 2020 programme since 1997 and will continue in that role with H2020's successor programme.
Drago has over 25 years’ experience in alternative investments and entrepreneurship and has worked as a portfolio manager and in research at several hedge funds, a family office and for a GCC sovereign wealth fund. He is an investor in a number of fintech businesses.
Drago lectures regularly on fintech and hedge funds at Queen Mary University of London and Regent's University. He holds a Dipl Ing from the University of Belgrade and a PhD from Imperial College, London; and is a Member of the IEEE and IET.
CPD / CPE Accreditation
This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.