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 virtual 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.
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 unsupervised, reinforcement and deep learning
Approaches to satisfy explainability
Identifying and monitoring risk in ML
Relevant departments may include but are not limited to:
Quantitative Development and Trading
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
Our live, virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:
- Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days
- Facilitated collaboration including Q&A, interactive polling and group workshops
- Live interaction with subject matter experts – get your questions answered in real time
- Receive comprehensive course materials and supporting content from Risk.net to reinforce your learning
- Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants
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.
Senior Manager, Data Scientist
Rogelio Cuevas is a Data Scientist, Senior Manager at TD Bank where his main responsibility is providing data science and machine learning solutions for different lines of business. Prior to his tenure with TD Bank, he was a Data Scientist at Scotiabank where he developed credit risk models in retail banking through the prototyping and implementation of machine learning techniques.
He is an active member of the Toronto data science community where he offers mentoring and training to professionals. Part of these activities include being a panelist at Rotman School of Business, mentor at Insight Data Science Toronto and invited speaker at University of Toronto.
Before his experience in the financial sector, Rogelio contributed with IBM through its Cognitive Class initiative, formerly known as Big Data University.
Rogelio has a strong academic background and research experience that he acquired working in academic institutions that include McMaster University, Duke University and The University of Western Ontario.
Vice President, AML / ATF Analytics
Vishal Gossain is currently the vice president of global risk management responsible for all global regulatory and non-regulatory retail models, and application of artificial intelligence and machine learning for retail products. Vishal has held previous senior retail modeling, risk management, business strategy, P&L management and finance positions in Latin America and North America. Some of them include Head of credit risk for HSBC in Latin America, Head of credit risk for Capital One Canada and several other risk/business management positions in HSBC USA and consulting firms.
Vishal currently sits on the board of MIT Computer Science and Artificial Intelligence Laboratory and University of Western Post-graduate program.
Vishal holds an undergraduate degree in engineering from Indian Institute of Technology (IIT) Kharagpur and has completed his post-graduation in engineering from University of Texas at Austin.
Maclear Data Solutions
Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.
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.