Name: Machine Learning in Finance
Start: 2022-02-28T08:00:00+00:00
End: 2022-03-03T12:00:00+00:00
Location: LIVE VIRTUAL | GMT/HKT TIMING

An event hosted by:

February 28 – March 3, 2022

LIVE VIRTUAL | GMT/HKT TIMING

Machine Learning in Finance

February 28 – March 3, 2022 | Live Virtual | Time zone: EMEA / APAC

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Advancements in machine learning (ML) applications have developed significantly in banking, risk management & modelling. During this 4-day interactive virtual course, will explore the types of unsupervised learning methods applied to financial data and recent advances in sequential and deep learning.

Sessions will focus on practical examples of ML for trading strategies including sentiment analysis for trading and back testing. Subject-matter expert Eric Tham will give participants the opportunity to discuss alternative data and the challenges, approaches, and practical use cases, associated with using it as well as how to use graphical ML in investment strategies.

The course will develop participants understanding of the metrics of explainability/ interpretability of using AI and behavioural and quantitative finance applications of this. Practical case study applications focusing on ML use cases will allow participants to apply the theoretical knowledge taught in the course sessions to real world examples

What will you learn?

Impact of recent approaches to finance and regulatory compliance for unsupervised learning when applied to financial data
Recent advances in sequential and deep learning
Best practices for creating and applying investment strategies with ML and graphical ML
Understanding the challenges of using alternative data with practical examples
Identifying the metrics of explainability/ interpretability of AI models
Practical examples of applying ML

Who should attend?

Relevant departments may include but are not limited to:

Machine learning
Risk management
Portfolio management
Data science
Financial engineering
Quantitative analytics
Quantitative modelling

Sessions include

Machine learning in finance: opportunities and limitations
Unsupervised learning applied to financial data
Recent advances in sequential and deep learning
Investment strategies with machine learning
Alternative data for traders and natural language processing
Graphical ML in finance
Explainable AI in finance
Applying machine learning in practice

Pricing options

We offer flexible pricing options for this course:

Early bird rates
Group rate
Enterprise rates
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Course tutor

Eric Tham

Senior Lecturer, Data Science

James Cook University Australia

Dr. Eric Tham is an experienced data science and finance professional with more than a decade of experience in the financial industry. He mixes practicum with his personal working experience in the Fintech and data science sectors. He has a passion to groom and train the next generation of data science professionals.

His research interests are at the intersection of behavioural finance and AI/ML, and have been presented at the American Economics Association (AEA/AFA) meetings, Miami Behavioral Finance conference, the European Financial Management Association (EFMA) meetings, the Australasian Banking and Finance conference (ABFC) and the Risk managment conference at NUS. He has won a research grant from the Think Forward Initiative for his research on social media trust.