Machine Learning in Finance Japan 2019

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Registrations are closed
Thank you for your interest in attending this event. We are no longer accepting online registrations however still have limited passes available. To secure one of the remaining passes, please contact Jayla Tam by email at Jayla.tam@infopro-digital.com or by phone on + 852 3411 4837. We look forward to welcoming you.

Event Information

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Date and time

Location

Location

FINOLAB

Otemachi Bldg 4F, Otemachi 1-6-1, Chiyoda

Tokyo, 100-0004

Japan

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Refund policy

Refund policy

No Refunds

Sales Have Ended

Registrations are closed
Thank you for your interest in attending this event. We are no longer accepting online registrations however still have limited passes available. To secure one of the remaining passes, please contact Jayla Tam by email at Jayla.tam@infopro-digital.com or by phone on + 852 3411 4837. We look forward to welcoming you.
Event description

Description

Event official website: https://training.risk.net/machinelearningjp


Through a quantitative approach, this training course thoroughly teaches technical aspects of machine learning usage, models and more advanced tools and solutions.

Date and time

Location

FINOLAB

Otemachi Bldg 4F, Otemachi 1-6-1, Chiyoda

Tokyo, 100-0004

Japan

View Map

Refund policy

No Refunds

Organiser Risk Training

Organiser of Machine Learning in Finance Japan 2019

Risk Training is a leading professional training provider specialising in risk management, regulation, derivatives, asset management and commodities. Our courses are held under the following brands: Risk.net, Insurance Risk, Operational Risk, Energy Risk, Inside Market Data and Inside Reference Data.

Our attendees come from a range of organisations including investment banks, academia, asset management, insurance, central banks and professional services.

With public training courses in London, New York, Toronto and Asia, our events provide a variety of industry perspectives and best practice approaches to meet the challenges that financial organisations face in the current regulatory landscape.

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