Machine Learning in Finance

Expand your knowledge of the recent trends, development, and application of machine learning in finance.

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Machine Learning in Finance

28 September - 1 October, 2020

View agenda   View prices

This virtual course, spread over four days, will provide attendees with a practical understanding of machine learning applications by exploring key theories, models and more advanced tools in machine learning solutions.

Delivered through practical, 60-minute sessions this course will provide insight into strategies for integrating ML solutions within your organisation as well as the impact of big data and risk on ML.

This course will build on attendees’ knowledge of neural nets and reinforcement learning while providing the opportunity for participants to interact with course leaders to explore different ML models, natural language processing and the impact of ML in portfolio construction and optimisation.

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What will you learn?
  • Understand the recent trends in ML application
  • Address the challenges presented by data analysis and big data for ML
  • The impact ML has on portfolio optimisation
  • How to apply neural networks in your organisation
  • Understand natural language processing models
  • The application of machine learning in banking, risk management and modelling
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Who should attend?

Relevant departments may include but are not limited to: 

  • Quantitative analysis
  • Financial engineering
  • Quantitative modelling
  • Data science
  • Machine learning
  • Portfolio management
  • Model risk
  • Risk management
Online training

Our live, virtual training courses have been designed to engage and inspire you. Much more than a webinar, our approach includes:

  • Technical content compressed into 60-minute interactive sessions and spread out over two, three or four days
  • Facilitated collaboration including Q&A, interactive polling and group workshops
  • Live interaction with subject matter experts – get your questions answered in real time
  • Receive comprehensive course materials and supporting content from Risk.net to reinforce your learning
  • Stay connected with other learners and extend your network by joining our dedicated LinkedIn group for course participants

Course Speakers

Jesús Calderón

Managing director

Maclear Data Solutions

Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in  banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.

Ranko Mosic

Big data architect

Bank of America

Ranko Mosic is a consultant specializing in AI/ML applied to finance. He is helping clients with ML/AI concepts, use case identification, feature selection, algorithm selection and build, technical assessment, recommendations and implementation. 

He advised State Street Corporation, Bank of America and other clients on ML/AI and Big Data initiatives. 

https://medium.com/@ranko.mosic

https://twitter.com/mosicr

[email protected]

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Greg Kirczenow

Senior director, enterprise model risk management

RBC

Greg is Senior Director in Enterprise Model Risk Management at RBC. He has a decade of experience in market and model risk management, with specialization in enterprise and retail risk. In his present role, Greg is leading efforts related to responsible AI practices, as well as development of validation techniques both for AI and using AI.

Zain Nasrullah

Data Scientist

RBC

Vathy Kamulete

Senior manager AI research - enterprise model risk management

RBC

CPD / CPE Accreditation

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CPD Accreditation

This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
CPE Member

CPE Accreditation

This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.
I really enjoyed the course. The instructors are superb and the material current and well presented.
ICAP
The training was very informative. I learned a lot from the course, and would definitely attend such course again.
Federal Reserve Board
I found the training to be very good. I thought the depth of coverage was appropriate.
US Securities and Exchange Commission
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