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Toronto Machine Learning Summit

It is our pleasure to welcome you to the 2018 Toronto Machine Learning Summit and Expo.

TMLS is a collaborative event for the Canadian Machine Learning community comprised of over 6,000 ML researchers, professionals and entrepreneurs across several disciplines.

Taken from the real-life experiences of our community, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight across 2- days, and 2-nights, this November 20th and 21st. 

Come expand your network with machine learning experts and furthering your own personal & professional development in this exciting and rewarding field. 

The TMLS initiative is dedicated to helping promote the development of AI/ML effectively, and responsibly across all Canadian Industries. As well, to help data practitioners, researchers and students fast-track their learning process and develop rewarding careers in the field of ML and AI. 

Location

The Carlu
444 Yonge St, Toronto, ON M5B 2H4

Hours

M-NOV 19th 5p–11p
T-Nov 20th 9am–9pm
W-Nov 21st 9am–9pm

 
 
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Latest Additions

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Thank you to our Sponsors

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Platinum Sponsor

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Gold Sponsor

Gold Diversity & Inclusion Sponsor

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Silver Sponsor and Best Poster Award Sponsor

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Silver Sponsor

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Silver Sponsor

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Silver Sponsor

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Silver Sponsor

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Career Fair Special Popcorn Sponsor

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Bronze Sponsor

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Bronze Sponsor

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Special Community Sponsor

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Community Sponsor

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Workshop Sponsor

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Community Sponsor

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Community Sponsor

 

SPEAKERS INCLUDE

 
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And More!

In the next five years, every company is going to need an AI strategy.
— Tiff Macklem, dean of the Rotman School of Management at the University of Toronto
 
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FAQ

 

Q: What's the refund policy? 

Tickets are refundable up to 30 days before the event.

Q: Why should I attend the TMLS?

Developments are happening fast - it's important to stay on top.

For businesses leaders, you will have direct contact with the people that matter most; consultants and experts, potential new hires, and potential new clients. For data practitioners, you'll have an opportunity to fast-track your learning process with access to relevant use-cases, and top quality speakers and instructors that you'll make lasting connections with while building your network.

The event is casual and tickets are priced to remove all barriers to entry. Space, however, is limited. 

Q: Who will attend? 

The event will have two tracks: One for Business, one for Practitioners.  Business Executives, PhD researchers, Engineers and Practitioners ranging from Beginner to Advanced (multiple project experience). 

Q: Will you focus on any industries in particular?

Yes, we will have talks that cover Finance, Healthcare, Retail, Transportation and other key industries where applied ML has made an impact. 

Q: I'm not sure artificial intelligence can benefit my business. Is this still relevant? 

Yes, a large component of the business track will be dedicated towards understanding the potential of machine learning and ensuring ROI. You can describe to us your project or problems and we will connect you with researchers and council. There will be many people to assist, on site. No additional charges. 

Q: Can my company have a display? 

Yes, there will be spaces for company displays. You can inquire at info@torontomachinelearning.com.

Q: Will tickets include access to the after-party? 

Yes, attendees will have full access to both night's post event networking social. 

Q: Where and how can I register?

Visit here to reserve your spot. 

Q: Will you give out the attendee list? 

No, we do our best to ensure attendees are not inundated with messages, We allow attendees to stay in contact through our slack channel and follow-up monthly socials.

Q: Can I speak at the event?

Yes you can always submit abstracts here. They will be considered for our future events.

Presentation guidelines are available here.

*Content is non-commercial and speaking spots cannot be purchased. 

Q: What's the date, time? 

Tuesday November 20th, 9am-10pm (including after-party) and Wednesday November 21st, 9am- 5pm (Relocate to local pub afterwards). We will also have a separate Bonus day dedicated for workshops on the 19th.

Q: Where is the event taking place? 

The event will take place at the historic Carlu at College and Yonge Street, Downtown Toronto; 7th floor, 444 Yonge St, Toronto, ON M5B 2H4. 

Q: Do you have a hotel block or discounts?

We do not have hotel discounts. 

Q: Who can I speak with for questions? 

You can visit here for more information, or email info@torontomachinelearning.com and somebody will be in contact within 24 hours. 

 

TMLS Code of Conduct


See Past Timetable

 

Venue

The Carlu, 444 Yonge St, Toronto, ON M5B 2H4, Canada

 
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ATTENDEES

Business Leaders, including C-level executives and non-tech leaders, will explore immediate opportunities, and define clear next steps for building their business advantage around their data. 

Practitioners will dissect technical approaches, case studies, tools, and techniques to explore Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML and more.

Job Seekers will have the opportunity to hone their skills as well as meet from over 50 Top AI Start-ups and companies during the EXPO & Career Fair

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Steering Committee Team

  • Frank Rudzick, Former 2018 TMLS Chairman. Associate Professor, University Of Toronto, Department Of Computer Science

  • Michael Guerzhoy, Sr. Data Scientist, Research and Training, St Michael’s Hospital, Assistant Professor, UofT

  • Karthik Ramakrishnan VP, Head of Industry Solutions & Advisory, Element AI

  • Laleh Soltan Ghoraie, TMLS, Poster Session Chair, Machine Learning Researcher, Borealis AI, Affiliate, Vector Inst.

  • Natalie Fratto, Vice President & Canadian Expansion at Silicon Valley Bank

  • Helen Kontozopoulos, Chief Product Officer & Co-Founder at ODAIA.AI. Adjunct Professor UofT

  • Devinder Kumar, TMLS Poster Session Chair, ML PhD Candidate, UWaterloo & Vector Inst. | Lead AI Scientist, NEXTAI

  • Ali Harakeh, ML PhD Candidate, UofT Institute for Aerospace Studies (UTIAS)

  • Asif Ahmed, Sr. Financial Analyst, The Home Depot

  • Laila Paszti, Attorney, AI Software Engineer

  • Rupam Mahmood, AI Research Lead, Kindred.ai

  • Matt Mcinnis, Data Scientist, Customer Success Unit, Microsoft

  • Jeremie Harris, Co-Founder, Sharpestminds

  • Geoffrey Hunter, Data Science, UberFlip

  • Ramy Nassar, Director, Retail Innovation Lab, Mattel, INC.

  • Amir Massoud Farahmand, Faculty Memeber, Vector Inst.

  • Max Huang, Data Scientist, Cerebri.ai

  • Alex Mildiwski, Sr. Manager, Client Analytics, Amimia Inc.

  • Elham Dolatabadi, Staff Scientist, Vector Institute

  • Diederik Van Liere, Director of Data Science and Engineering, Ritual

  • Ozge Yeloglu, Chief Data Scientist, Customer Success Unit, Microsoft Canada

  • Ajinkya Kulkarni, Senior Director, Data Science and AI at Scale, RBC

  • Abhinav Kaushik,Workforce Operations, Strategy and Analytics, The Home Depot

  • Wanda Peteanu, Director of Information Management, The University Network (UHN)

  • Shahrukh Islam, Sr. Consultant, AI Strategy at Omnia AI, Deloitte Canada

  • Joseph Kurian, Sr. Advisor- Advanced Technologies, Province of Ontario (Ministry of Economic Development, Job Creation and Trade)

 

TMLS Team

  • David Scharbach, Director, founder TMLS

  • Amir Feizpour, Data Scientist, Scientific Advisor, RBC

  • Tina Aprile, Entrepreneur, Organizer TMLS

  • April Grace de las Alas, Structural Coordinator, TMLS

  • Diego Cantor, Ph.D, Head of Presentations TMLS, Founder, CTO, EZRA

  • Sean Robertson, Associate, Syntegrity

  • Amy Mansell, Organizer TMLS, project Manager, Lighthouse Labs

  • Maecy Santos, EA TMLS

  • Faraz Thambi, Marketing & Partnerships

  • Maria D'Angelo, Ph.D, Head of Diversity & Inclusion Programs TMLS, Data Scientist, Delphia

  • Reena Shaw, Head of Editorial Content, Lead Data Scientists, Agile Blockchain

  • Julia Mariglia, Manager TMLS Newsletter