Generative AI masterclass
View AgendaKey reasons to attend
- Learn use cases of generative artificial intelligence (GenAI)
- Explore the implications of GenAI hallucinations
- Address the importance of neural networks architecture
Customised solutions
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About the course
Join us on this virtual masterclass to gain a robust understanding of GenAI. Participants will deep dive into the technicalities of GenAI by studying the mathematics of ANNs and the ethical aspects of managing AI.
Key sessions will cover fundamental topics that provide diverse views of GenAI from a financial industry perspective. Attention mechanisms in LLM frameworks will also be explored.
Participants will engage in practical case studies and discussions alongside their peers and the expert tutor, while learning how to navigate the opportunities and risks of this rapidly changing technology.
Pricing options:
- Early-bird rate: save up to $800 per person by booking in advance*
- 3-for-2 rate: save over $2,000 by booking a group of three attendees*
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber*
- Season tickets: cost-effective option for groups of 10 or more. Learn more
*T&Cs apply
Learning objectives
- Apply GenAI by studying a sample Python notebook
- Evaluate large language models (LLMs) and their basic criteria
- Determine how to construct a LangChain and a LangGraph
- Assess artificial neural network (ANN) frameworks
- Navigate how to achieve more complex and sophisticated outputs
- Align on the legal and key ethical aspects of AI
Who should attend
Relevant departments may include, but are not limited to:
- Risk management
- AI
- Machine learning
- Compliance
- Regulation
- IT and data management
- Chief information security officers
- Innovation
- Risk technology
Tutors
Sunil Verma
Front office quant supporting business decision making
Citi
Sunil is a former risk and front office quant and is now embedded in a desk responsible for in-business risk management within Citi Markets. He has held multiple positions within investment banking and has been largely focussed on market and counterparty risk related modelling activities. He has also worked extensively on business efficiency and regulatory focussed projects.
He has over 20 years of experience in the industry in various roles involving mathematical modelling, coding, project management, team coaching, etc.
Sunil was previously working at UBS and headed their market risk stress testing initiatives. He is a hands-on quant with Python being his coding language of choice.
Sunil’s qualifications include an Engineering degree and an MBA from IIT Mumbai. He lives in London and enjoys walks with his dog.
Pre-reading materials
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