Abstract: 

This workshop will provide an overview of how to use machine learning to forecast complex operational problems. This workshop is a hands-on, Python-based, introduction to how machine learning can be used to tackle time-series problems. Topics to be covered include: why time-aware ML problems are different from non-time-aware ML problems; why time-series and forecasting problems in particular are challenging; and how to use to leverage both deep-learning and non-deep-learning approaches to successfully tackle real world problems. This course is a code-based workshop using a variety of Python based tools so familiarity with Python and data science fundamentals will be helpful, but time series experience is not assumed.

Bio: 

As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.