Connecting Industry Problems With Machine Learning Startup Innovations

October 26, 2016

DoubleTree Downtown Toronto

Produced by

MLX: the Machine Learning Exchange was founded as the Toronto based hub to connect Industry problems with startup machine learning solutions and talent.


University of Toronto is the leading machine learning institution in the world, with world renowned researchers and many of its machine learning graduates have gone on to head major machine learning initiatives at Google, Uber, OpenAI, Microsoft, IBM, Facebook and many others. Equally, Rotman is a leading Business School with world recognized leaders in Finance and Risk Management, producing sought after graduates. The talent is here and they shouldn’t have to go to Silicon Valley to create game changing financial services solutions, nor should Canadian based Financial Services firms have to go to Silicon Valley to employ that talent or partner with startups to achieve a competitive advantage. MLX is dedicate to making that connection.


MLX will host conferences, round-tables, workshops and meet ups, a talent exchange , courses and targeted training.


This MLX Fintech Conference is the inaugural conference and part of a series which will include finance, health, security and IoT,



About Machine Learning

Traditional problem solving focuses on people using domain expertise to engineer custom solutions for every situation they are planning to encounter. Machine learning is a field at the intersection between statistics and computer science that deals with the design of systems that have the capacity to learn how to solve general problems. Rather than relying on people to provide domain expertise, the goal of machine learning is to produce software that can learn domain expertise by extracting complex patterns from data.

For example, AlphaGo is a software agent designed by Google DeepMind that learned how to play Go better than the world champion when initially it didn't even know the rules of the game. It was built to be relatively general-purpose; there is nothing in the technology that prevents it from being applied to other problems. Reinforcement learning, the underlying branch of machine learning that was used to train AlphaGo, is being used to develop autonomous vehicles, which is a noticeably different problem than playing Go.

Recent advances in machine learning have already been deployed to solve tough problems involving image, text and speech processing. The development of the science and technology of machine learning has opened the door to a sea potential applications and opportunities. There is no question that machine learning will become the basis of new analytic and cognitive technology in the near future, if it isn't already.

Machine Learning and Fintech


Financial Institutions have access to large pools of both public and private data. This abundance of data presents many opportunities for potential machine learning applications. Possible application areas include customer segmentation, sales or cross-selling in retail banking, improved customer experience for retail banking, fraud detection, anti-money laundering and trading and capital markets. For example, there are many startups leveraging machine learning to perform sentiment analysis of news for trading, or using systems that use clustering to obtain insights into data so that they can better serve their customers.


Save the date

October 26, 2016: Fintech

March 15, 2017: Health

June 21, 2017: Security

October 25, 2017: IoT/Robotics






Machine Learning

Fintech Conference