Machine learning applications in finance
View AgendaKey reasons to attend
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Identify current industry data-driven approaches
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Understand machine learning techniques used to maximise profits
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Align supervised and unsupervised learning methods
Customised solutions
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Working with the portfolio of expert tutors and Risk.net’s editorial team, we can develop and deliver a customised learning to make the most impact for your team, from initial assessment to final review.
About the course
This interactive learning event allows participants to learn the best practices of machine learning by focusing on supervised learning models, neural nets and further machine learning methods. Participants will identify core components essential to a successful machine leaning application.
Led by the expert tutor, sessions will provide practical insights on the challenges machine learning presents for financial institutions. Interactive sessions will connect the expert tutor and participants through active discussion, Q&As and practical case studies.
Participants will deep-dive into the application of machine learning in risk management and strengthen their understanding of integrating data science teams into the machine learning process.
A basic understanding of statistics and data manipulation is required for participation in this event.
Flexible pricing options:
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Early-bird rate: book in advance and save $200
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3-for-2 group rate: book three delegates for the price of two and save more than $2,000
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Season tickets: book a team of 10 or more and save up to 50%
Learning objectives
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Assess recent advances in sequential learning and deep learning
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Interpret the early financial applications of machine learning
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Employ the best practices of explainability and interpretability of AI models
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Apply back-testing strategies with machine learning
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Utilise alternative data that helps capture valuable information
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Approach finance and regulatory compliance from different types of machine learning perspectives
Who should attend
Relevant departments may include but are not limited to:
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Machine learning
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Risk management
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Portfolio management
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Data science
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Financial engineering
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Quantitative analytics
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Quantitative modelling
Agenda
June 19–21, 2023
Timezones: Emea/Apac
Sessions:
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Introduction to machine learning in finance
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Types of machine learning applied to financial data
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Deep learning and sequential learning
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Alternative data and natural language processing (NLP)
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Investment strategies with machine learning
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Graphical machine learning in finance
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Integrating machine learning in risk management
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Explainable AI in finance
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Applying machine learning in practice
October 9–11, 2023
Timezones: Emea/Americas
Tutors
Eric Tham Risk Learning Faculty
senior lecturer, data science and fintech
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
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Why machine learning quants need ‘golden’ datasets - Read article | Risk.net
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Machine learning models: the new standard in capital markets - Read article | Risk.net
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Alternatives to deep neural networks in finance – CROs - Read article | Risk.net
To access some of the above articles you need to have a current subscription to Risk.net. If you don’t have one now, please subscribe to a free trial
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