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Overview of the event

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn from data, without being explicitly programmed.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to its original goals. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.

Facts & Figures About The Event

7 Speakers
7 Topics
50 Tickets

Conference Schedule

Machine Learning and Deep Learning Day (10th October, 2019)
X Topic Abstract

This talk will cover basic terminology, 'realistic' benefits and challenges, how to develop an enterprise strategy, designing and implementing it, enabling individual functional teams, and finally cultural and ethical aspects.

X Topic Abstract

One of the most successful applications of deep learning models are in the area of computer vision applications. The capabilities of the current smart phones let us to employ the power the smartphone hardware to perform a real-time detection, recognition, and predictions. Furthermore, the latest Deep Learning libraries, such as TensorFlow, provided an easy straight way to create high-end applications. Form another point of view, the internet access helps end-users to interact directly with Deep Learning model APIs to perform much more complicated predictions and incremental machine learning has not been easier than it is today. As a case study, Sina Media Lab and ADERSIM are developing Earthquake Smart Space application which lets users scan their environment using the smartphone cameras anywhere in the globe to let them estimate the risk on an injury in the case of an earthquake.

X Topic Abstract

The expected credit loss under International Financial Reporting Standard 9 (IFRS9) is estimated as the weighted average of the scenario losses. In practice, the weights are assigned by internal entities rather than by a consensus of experts or other outside bodies. This calls into question the incentive compatibility of the weights being set. We are proposing algorithms, which are theoretically derived, to find the optimal weights such that the weighted average of the scenario losses aligns with the empirically observed expected loss.

Speaker Profile

Zunwei Du is an associate director at RBC, with responsibilities for IFRS 9 and stress testing programs including the design of macroeconomic and financial scenarios and the development of credit risk models and risk aggregation engines. She has also been responsible for reverse stress testing and ICAAP. Previously, she developed a liquidity model for a fixed income fund at RBC Global Asset Management. She has published in the areas of rating-transition-probability model for stress testing and scenario loss weight estimation for IFRS 9. She received her Master of Financial Economics and Bachelor of Commerce from University of Toronto.

X Topic Abstract

A high level overview from an Machine Learning and Analytics implementation consultant that outlines practical uses of analytics that every organization should have and how to get there. Will tell story on implementation, share frameworks and common road maps organization uses to achieve excellence using data and advanced analytics.

X Topic Abstract

Quantum computing has had some progress recently, both in hardware, software, and applications that show promising enhancement of the machine learning landscape. In this talk, I will discuss some fundamental concepts and operations with quantum machine which can be used in the near future with noisy-intermediate scale quantum machine, especially in the domain of machine learning.

Speaker bio

X Topic Abstract

We will discuss a number of effective ways to accelerate the pace of business transformation through Intelligent Automation, that incorporates Digitization, Artificial Intelligence, Machine Learning, and a number of other advanced tools. We will provide details of some practical examples where advanced capabilities have considerably improved our pace of transformation. We will also showcase a number of practical examples of potential challenges with AI, ML and other advanced capabilities, and the solutions (sometimes decidedly low-tech process changes) that have helped mitigate these risks & challenges.

Our Sponsors

Use this opportunity to improve the visibility of your organization

Instant Sponsorship
Avail instant sponsorship at just USD 2000

Instant sponsorship includes
• Branding of your company as Bronze Sponsor – Company's Logo on the event page with cross link to your website.
• One Speaking Slot (45 min -non sales talk).
• 10% discount on registration fee for any delegate from your organization.
• Full day attendance at the event with lunch
• 1 x Roll up stand / Brochure distribution at the event
• Online Interview post of your company's senior executive at our media portal

For Silver, Gold Platinum & Titanium Sponsorship opportunites, please request for Sponsorship Brochure via email at contact@1point21gws.com, naveen@1point21gws.info

Our Sponsors

Media Partners

Association partner

Digital Marketing Partner

Marketing Partner

Promotional & Media Partners

Our Past Sponsors

Media Partner


Our Speakers

Manish Kanwal

Artifical Intelligence & Machine Learning Specialist – Senior Product Manager

Adobe Systems

Zunwei DU

Associate Director, Stress Testing Analytics


Eric Huang


Advanced Analytics and Research Lab

Ghassem Tofighi

Professor of Cognitive Computing and Machine Learning, School of Applied Computing

Sheridan College

Hugh Frost

Practice Leader – Intelligent Automation, Digitization & AI

The Burnie Group

Want to become Speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

CAD 400

Till July 27
Group of three or more(Standard)

CAD 450

Till October 10
Individual(Early Bird)

CAD 600

Till September 10
Individual (Standard)

CAD 750

Till October 10

To register Via Eventbrite

Our Testimonial


Who can attend Machine Learning and Deep Learning Day in Toronto?

• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• Scientists/Researchers
• Professors
• President/Vice president
• Chairs/Directors

And last but not the least……….
Anyone interested in Machine Learning & thrives to make the future developed and better

Why to attend Machine Learning and Deep Learning Day in Toronto?

Understand the state of development of Machine learning by exchanges, clearing houses, central counter parties and payment systems, and what it means for you.

What will you learn about?

Detecting where underlying problems and frictions exist in your organisation that will be alleviated by Machine learning technologies. Using Machine learning as a tool for innovation across your organisation

Are there any prerequisites to attend this program?


Do I need to register for the event?

Yes, all conference attendees must register in advance to attend the event.