Automated machine learning with Azure AutoML // Evolving decision trees


Part 1: Automated machine learning with Azure AutoML
Speaker : Rahat Yasir
AutomatedML is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML democratizes the machine learning model development process, and empowers its users, no matter their data science expertise, to identify an end-to-end machine learning pipeline for any problem. In this session, we will talk about Automated Machine Learning process and also will give step by step demo of Azure AutoML.

Bio: Rahat Yasir is Microsoft Most Valuable Professional in Artificial Intelligence. He got Canada's Dev 30 under 30 Award in 2018. He is currently working at Intact Financial Corp as Machine Learning and AI Engineer.

Part 2 : Building evolving decision trees with C#
Speaker: Frederic Simard

If you have sufficient data, you can build decision tree dynamically with algorithms such as the ID3 algorithm. What if, however, some parts of those decision trees are static, or subject to rules that are not entirely data driven?

In this topic I'll show how you can use techniques such as reflection to be build decision trees that will evolve, using a mix of data and rules.