Machine Learning in Quantitative Finance

Machine Learning in Risk Management and Financial Applications; Implementing machine learning models; Neural Nets and Deep Learning.

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

The course will feature examples of the practical applications of machine learning which can help you to extract real value from this technology to meet your business objectives, with an interactive 60-minute code review session each day covering essential concepts and use cases of machine learning in financial applications. 

The course will be led by subject matter expert Jesus Calderon; featuring in-depth and interactive presentations covering topics such as neural nets, deep learning, reinforcement learning and explainability. 

There will be opportunities throughout the course to receive guidance on your specific learning objectives from the course tutor and engage in discussion with other attendees. 

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.

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What will you learn?
  • Gain a comprehensive introduction to machine learning models, their applications in finance and regulatory implications 
  • Receive guidance to develop a robust roadmap for implementing machine learning models
  • Understand how to achieve explainability in machine learning 
  • In-depth insights into neural nets and deep learning
  • Gain a thorough understanding of unsupervised methods and reinforcement learning
  • Understand the challenges of anomaly detection
  • Each day ends with an engaging code review session on the discussed concepts theoretically – code is previously distributed with participants
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Who should attend

Relevant departments may include but are not limited to: 

  • Quantitative finance
  • Derivative pricing 
  • Model risk 
  • Model validation 
  • Regulators and compliance  
  • Data science

*Pre-understanding in machine learning and quantitative finance is required. 

Live Virtual training courses

 

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

CPD and CPE Accreditation

 

CPD Accreditation

This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 8 credits. One credit is awarded for every hour of learning at the event.

CPE Accreditation

This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 8 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.

Not the course for you?

Risk Training offers a great selection of courses providing practical guidance on the latest trends, challenges and regulatory changes that span risk management, regulation and derivatives.

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E-Learning

Risk Training’s self-paced E-Learning platform offers Essentials of Operational Risk programme, plus more topics to come soon.
 

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