As a researcher, Mark has published works on blockchain technology for supply chain finance, graph deep learning for anti-money laundering, and algorithmic fairness for anti-discrimination in lending. Background: Pricing is a famous business issue in many companies and organizations. The audience will also get insights into how edge computing, edge analytics and fog computing can be leveraged by Intrusion Detection systems for security analytics at the edge for IoT. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. She develops Tractable’s deep learning algorithms, and focuses on diversifying and scaling the core AI across domains. Nathan Killoran is the Head of Software & Algorithms at Xanadu, and one of the founding developers of PennyLane, the world’s leading quantum machine learning software library. Black Lives Matter. The application of Deep Learning to Derivative Valuation Adjustments. In this talk, we will discuss the evolution of autonomous robots for space exploration and planetary science. to assess A.I. Saeed Aghabozorgi Ph.D. is senior ML Specialist in AWS, with a track record of developing enterprise level solutions that substantially increase customers’ ability to turn their data into actionable knowledge. The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems was launched on May 16, 2018 at RightsCon Toronto.. 4. Neural machine translation, applications of machine learning to Indigenous languages, challenges of domain adaptation in low-resource settings. Patrick is the Director of Data Science at the Washington Post. She is an instructor at Amazon Machine Learning University and frequently presents at external events such as AWS Re:invent, Nvidia GTC, etc. Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI. Experience managing/leading technical teams and/or organizational initiatives are helpful recommended. Toronto Conferences Search 1,207 Machine Learning jobs now available in Toronto, ON on Indeed.com, the world's largest job site. From electric/autonomous vehicles to politics she has written and spoken about public policy regarding transportation, innovation and technology as well as provided strategic assistance for transportation associations and companies. As pricing is very critical, mainly companies do not reveal their methodology so google search will not help that much. I will present a novel method for generating synthetic datasets (which has not yet been published) as well as 2 real world case studies of Arima's partners on how synthetic data has improved their model performances. I would like to share how this ecosystem (ML, Robotics and process engineering) will result in significant benefits for the organization. Sentiment analysis, text classification, Named Entity recognition and QA using Bert and Spacy Models. These huge complex models trained on billions of words of text have been made available to researchers and industry to solve real-world problems. To conquer these difficulties, we enrich millions of transactions from a variety of sources using data build tool (DBT) while ensuring quality checks. Executives, analysts, engineers, and developers all want to leverage the power of AI to gain better insights and make better predictions. Project Manager at Applied AI Project, Vector Institute. She is an Associate Fellow at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, and Visiting Research Fellow at the S. Rajaratnam School of International Studies at the Nanyang Technological University. How Social Media Encourages Consumption. Learn how they built a machine learning system for automatically moderating comments from millions of readers. Cody is a data scientist on the online visual intelligence team at the Home Depot. In many cases our graphical strategies generated steadily increasing returns with low risk and outgrew the S&P 500 index. The audience will learn about how practical applications of NLP are incorporated into the investment research process in order to generate alpha on a discretionary and systematic basis. Technically - this will include discussion of the application of Transformer based classification algorithms, trained under weak supervision into a Black-Litterman Portfolio Optimization process. Space, however, is limited. Talk: The State of AI/ML at the United Nations. Dana Movshovitz-Attias is a Staff Software Engineer and Researcher at Google Research, where she leads an NLP research group focused on Conversational AI, Graph ML, and Efficient ML Computation. Hello everyone, my name is Dean Van Asseldonk. He did his PhD in Atomic and Condensed Matter Physics from Cornell, and worked as a research physicist at ExxonMobil building machine learning models for oil and gas exploration. Deploy machine learning algorithms to mine your data. Mitigating bias in machine learning systems is crucial to successfully achieve our mission to "bring everyone the inspiration to create a life they love". ML in Production- Applied Case Study Talk: Building an AI Engine for Time Series Data AnalyticsJian Chang, Senior Algorithm Expert, Alibaba Group, Advanced Research Talk: Lookahead Optimizer: k steps forward, 1 step backMichael Zhang Researcher, University of Toronto & Vector Institute, Business Talk: Harnessing Graph-native Algorithms to Enhance Machine Learning: A PrimerBrandy Freitas Senior Data Scientist, Pitney Bowes, Applied Case Study Talk: Applications of AI in medicine: roadblocks and opportunitiesNiki Athanasiadou, Data scientist, H2O.ai, Advanced Research Talk: Differential Equations for Irregularly-Sampled Time Series Differential EquationsDavid Duvenaud, Assistant Professor University of Toronto, Vector Institute, Business Talk: Trustworthy AI: Model Validation at ScaleLayli Goldoozian, Data Scientist, Lucy Liu, Director, Greg Kirczenow, Senior Director, Enterprise Model Risk Management, RBC, Applied ML Talk: An Explanation of What, Why, and how of Explainable AI (XAI)Bahador Khaleghi, Customer Data Scientist and Solution Engineer, H2O.ai, Advanced Technical Talk: HoloClean: A Scalable Prediction Engine for Automating Structured Data PrepIhab Ilyas Founder, Professor, Tamr, University of Waterloo, Business Talk: Lessons from Google's Journey to AI-FirstChanchal Chatterjee, Leader in Artificial Intelligence Solutions, Google, Applied ML in Production Use Case: Scaling Machine Learning - Choosing the Right ApproachRazvan Peteanu Lead Architect, Machine Learning, TD Securities, Advanced Technical Talk: Image Augmentations for Semantic Segmentation and Object DetectionVladimir Iglovikov, Senior Computer Vision Engineer, Lyft, Business Panel: Determining Which ML Opportunities You Should PrioritizeTomi Poutanen, Chief AI Officer, TD, Founder, Layer 6 AI, Simona Gandrabur, Sr. Director, AI Lead at the National Bank of Canada, Wealth Division- Ofer Shai Chief AI Officer Deloitte, Omnia AI, Rupinder Dhillon- Chief Data Officer, SVP Data & AI, Hudson's Bay Company. Methodology: We propose to use model based recursive partitioning (MOB) which use product characteristics and customer attributes as input and customer willingness to pay as output to segment customers. The talk will also discuss how big data and cloud is used for security analytics at scale both for IoT and enterprise security. We anticipate that FTL will enable the machine learning community to benefit from large datasets with uncertain labels in fields such as biology and medicine. He was the Nomura Professor of Mathematical Finance, the Director of Nomura Center for Mathematical Finance, and the Director of Oxford-Nie Financial Big Data Lab at the University of Oxford during 2007-2016 before joining Columbia. As the field continues to advance, responsibility is becoming increasingly important to meet expectations of all stakeholders. Machine learning explainability has become an active area of academic research and an industry in its own right. Q: What's the refund policy? Business Executives, PhD researchers, Engineers and Practitioners ranging from Beginner to Advanced. In particular, he has published 75+ research papers in reputed journals on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; theoretical physics, jet-engines, and smart building systems). Talk: Artificial Intelligence for Molecular Design and Self Driving Labs. In this talk, I will give a high-level overview of the key ideas that make this possible. Previously, Wenming had a diverse R&D experience at Microsoft Research, SQL engineering team, and successful startups. Pointed lessons learned and unique insights from leading data science organizations will be shared covering how to effectively manage your people, your process, and your technology. Jaya Kawale is the Director of Machine Learning at Tubi leading all of the machine learning efforts at Tubi encompassing homepage recommendations, content understanding and ads. Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was founder and CTO of Tooso, an A.I. You will learn advanced modeling and algorithmic techniques for financial data and the gains obtained by using tailored approaches. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. degree in Robotics from the Robotics Institute, Carnegie Mellon University, USA, and his B.S. Azin Asgarian is currently an Applied Research Scientist on Georgian’s R&D team where she works with companies to help adopt applied research techniques to overcome business challenges. Corey has a passion for using data to make better sense of the world. In 2015, Dr Deng founded Beijing RxThinking Inc, applying deep reinforcement learning cutting-edge technology to solve healthcare problems. Still, a main challenge is to transfer research findings into actual AI products. Aim: Our main objectives is to design a pricing product that can help to: 1) Identify groups of elastic and inelastic customers, 2) Determine the optimal rate for each group of customers, 3) Be agonistic pipeline and can be reusable for other pricing use cases. Despite all the progress that has been made, machine learning explainers are still fraught with weakness and complexity. *Content is non-commercial and speaking spots cannot be purchased. In this talk, we show how to use A.I. We will share the technical challenges with building the comment moderation platform and how we raised the quality of online conversations with machine learning. Before coming to Treasury she studied backer behavior and what makes projects successful at Kickstarter. In this talk, I will argue that what we need is an interpretable machine learning model, one that is self-explanatory and inherently interpretable. Machine learning (ML) as an academic research field is over 60 years old. You can inquire at info@torontomachinelearning.com. Prior to founding Arima, Winston was the Director of Data Science at PwC and Omnicom Mediacom. Artificial Intelligence (AI) and Machine learning (ML) have exploded in importance in recent years and garnered attention in a wide variety of application areas, including computer vision (e.g., image recognition), game playing (e.g., AlphaGo), autonomous driving, speech recognition, customer preference elicitation, bioinformatics (e.g., gene analysis) and others. Q: Are there ID or minimum age requirements to enter the event?There is not. Before venturing into industry, Randi completed a PhD in Astrophysics at UT Austin, including research on both active galactic nuclei and how students learn astronomy, which gave her experience with varied statistical data-mining techniques and many kinds of data sets. Improve your business decision-making using analytical models. Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more. Our faculty and students do everything from creating low-cost digital x-ray imagers to combat tuberculosis in developing countries, to