November 28-29, New York.
This two day training course will provide delegates with an in-depth understanding of machine learning applications. This course is a perfect starting point for those who want to catch up with the machine learning momentum and grasp the potential of its applications in your organization.
The multi-tutor format will provide attendees with an understanding of fundamentals, practical applications and more advanced tools in machine learning solutions throughout the business lines including risk, trading, portfolio construction and beyond.
- Unique multi speaker format featuring sessions from key industry practitioners and academics
- Get insights into the big data revolution and the building blocks of machine learning tools in finance
- Understand fundamentals of machine learning methodology
- Learn the theory and practice behind machine learning, deep learning and neural networks, and how can these methods be applied in your organization
- Gain insights into the latest and most widely used industry applications
- Get a clear view of ML/AI capabilities in finance, how they can help you solve problems more effectively and drive your business forward
- Understand where the industry currently stands with regards to machine learning applications in finance
- Get an introduction to classical and advanced models
- Learn about capabilities of machine learning tools in portfolio construction, trading, risk management and beyond
- Get to grips with the modern data analysis - structured and unstructured data and new models
- Understand challenges related to data infrastructure and technology
- Discuss challenges, opportunities and the future of machine learning in capital markets
Who should attend:
This course is primarily aimed at those working in financial institutions; as well as regulatory bodies, advisory firms and technology vendors. However Risk welcomes anyone who would benefit from this training. Specific job titles may include but are not limited to:
- Portfolio management/asset allocation
- Risk management/modeling
- Data science
- Financial engineering
- Quantitative analytics/modeling
- Infrastructure and technology