Andreas C Mueller
Andreas Mueller is a Principal Research SDE at Microsoft, where he works on the interface of the Data Science ecosystem and cloud infrastructure as a member of the Gray System Lab. He previously held positions as Associate Research Scientist at the Columbia Data Science Institute and as a Research Engineer at the NYU Center for Data Science. He is one of the core developers of the scikit-learn machine learning library, a member of the scikit-learn technical committee, and the author of the book "Introduction to machine learning with Python".
Sessions
In this session, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library named FLAML. We will start with an overview of the AutoML problem and the FLAML library. We will then introduce the hyperparameter optimization methods empowering the strong performance of FLAML. We will also demonstrate how to make the best use of FLAML to perform automated machine learning and hyperparameter tuning in various applications with the help of rich customization choices and advanced functionalities provided by FLAML. At last, we will share several new features of the library based on our latest research and development work around FLAML and close the tutorial with open problems and challenges learned from AutoML practice.