Event detail

Name: Machine Learning & AI in Quantitative Finance Conference 2017

From: 15 Nov 2017

To: 17 Nov 2017

Address: Kingsway Hall Hotel London, United Kingdom

Organizer: Worldwide Business Research

Key speakers: Yves Hilpisch, Managing Partner, The Python Quants Ediz Ozkaya, Executive Director & Machine Learning Strategist, Goldman Sachs Lawrence Edwards, Executive Director, Morgan Stanley Kwasi Affum, Vice President of Regulatory Impact Assessment, Barclays Roland Fejfar, Vice President of FinTech IBD, Morgan Stanley Paul Bilokon, Senior Quantitative Consultant, BNP Paribas Alexei Kondratyev, Managing Director Financial Markets, Standard Chartered Bank Ignacio Ruiz, Founder & CEO, MoCaX Intelligence Claudi Camps, Deep Learning Specialist, ABN AMRO Clearing Bank N.V. Daniel Drummer, Vice President of Corporate & Investment Bank FinTech, J.P. Morgan Miquel Noguer, Executive Director, UBS Abdel Lanterem Data Scientist & Quantitative Consultant, HSBC, Andres Hernandez, Manager & Financial Services Risk Consulting, PwC Marleen Meier, Quantitative Risk Analyst & Data Visualization, ABN AMRO Clearing Bank N.V.

Web page: https://wbstraining.com/php/conferences/?id=174

Pricing: - variable

Description: Topics:

Predictive Power vs. Expressiveness of Machine Learning Models
Challenges with opaque ML models
Machine Learning in Quantitative Finance
Changing role of quants – from derivatives modellers to data scientists
Machine learning in finance - Practice
Black-box Machine Learning: Improving Transparency
Learning Curve Dynamics with Artificial Neural Networks
Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists
Machine learning - Deep learning
What are the modelling applications which benefit from deep neural networks?
Co-creating Machine Learning solutions within a global Corporate & Investment Bank
Unsupervised Anomaly Detection in Finance
Financial Singularity -- Paths, Dangers, Strategies
Financial Time-Series Regime Detection
What new insights can Machine Learning offer into the analysis of financial time series?
Machine Learning at Central Banks
Machine Learning and Regulation: From Regulating Machines to Regulation by Machines

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