| CFP closed at | September 10, 2017 08:09 UTC |
| (Local) |
Conference Format: Two days, one night (Nov 2nd - 3rd, 2017) Designed to serve both AI/ML professionals and industry seeking to bring value to their organizations through implementing AI/ML, the official content of the conference will consist of talks, panels and breakout/workshop sessions. Additionally there will be a heavy emphasis on opportunities to connect within the community. The first afternoon will provide the opportunity for AI practitioners, start-ups and industry to mix in the concourse hall and then, after the event, at a social gathering off-site. Official schedule to be announced.
Call for Papers (Talks): For clarity, the call for papers is a request for TALK, PANEL, and WORKSHOP abstracts only. Please do not submit poster proposals or full academic publications - we do, however, welcome technical AI/ML talks, panels, and workshops designed for an expert audience.
About Us: Toronto Machine Learning represents a community of Universities, Institutions, Startups, Enterprises, and Individuals working together to build on Canada’s AI successes. Our vision is to cement Canada’s position global leader in advancing AI. We intend to accomplish this by fostering an environment where industry and academia collaborate to accelerate investment, incubation, training and job creation, and to plant the rallying flag for this digital transformation squarely on Canadian soil.
Based in Toronto - Canada’s most populous city and home to premier universities and innovation hubs - the goals of this conference are manifold: to connect machine Learning and Data Science experts and fuel collaboration and cross-pollination; to educate Industry on preparing to harness AI – what to expect, how to prepare their data sets, and how to vet new hires; and to nurture the training and growth of the next wave of home-grown professional talent to join our community of world leaders in AI.
CFP Description
Frequently Requested Talks, Workshops, and Panels:
For Practitioners: (Beginner/Intermediate/Expert)
Interesting Problems and Innovative Solutions – Business cases – Using Tensorflow – Applied Reinforcement Learning – Applied Generative Adversarial Networks – Auto Machine Learning – Using AWS – Docker Containers – Using and Best Practices (Spark, Keras, Panda, etc.) – Visualization Methods for Big Data (What to use, when) – Exploration of Business Value in Big Data using ML/AI – Story: How You Helped your Employers/Businesses become data-driven – Using Data to Help Make Important Contributions to your Business (informed decisions) – Tips/tricks to Best Implement AI Using Open-source Recommendation Engines
For Business:
Becoming AI ready: A step by step approach to what you can do – Setting up/Preparing your Data (Pre-processing/Tagging/data cleaning/designing these systems) – Designing systems and models – Contextual Information that Needs to be Applied to Data – Unifying Data Sources – High-level Exploration of Contributions of AI/ML in Canadian Industries – Economic Opportunities for Canadians on a Global scale –Should you hire a data scientist? What Kind/Specialty? – Better business through data analytics – Exploring Business Value in Big Data using ML/AI – Revenue Models for Implementing AI – Implementing AI for Small/Medium Businesses – Machine Learning as a Service? – Assessing and Getting set up (Non-technical, accessible language)
Overview: We’ve seen some pretty incredible topics come into our CFP, but we often hear from smart and talented professionals that they don’t submit because they can’t think of a topic. In this growing field, it is almost guaranteed that you take your skills and knowledge for granted and think everyone already knows what you know. This is probably not the case. We made a list of frequently requested topics to help you remember that you do know a lot that others would like to hear about.