U.S. Department of Energy

Pacific Northwest National Laboratory

Better Machine Learning Through Data

Friday, March 3, 2017
Dr. Saleema Amershi
Researcher, Machine Teaching Group
Microsoft Research
Machine learning is the product of both an algorithm and data. While machine learning research tends to focus on algorithmic advances, taking the data as given, machine learning practice is quite the opposite. Most of the influence practitioners have in using machine learning to build predictive models comes through interacting with data, including crafting the data used for training and examining results on new data to inform future iterations. In this talk, I will present tools and techniques we have been developing in the Machine Teaching Group at Microsoft Research to support the model building process. I will then discuss some of the open challenges and opportunities in improving the practice of machine learning.
Speaker Bio

Saleema Amershi is a Researcher in the Machine Teaching Group at Microsoft Research. “Machine teaching” is machine learning with a focus on increasing user, or “teacher,” productivity and effectiveness. Saleema’s research lies at the intersection of human-computer interaction and machine learning. In particular, she creates tools to support both practitioner and end-user interaction with machine learning systems. Examples include general purpose tools to support data scientists and machine learning experts building reusable predictive models for production use and application specific tools to support the average person interactive with machine learning in their everyday lives (e.g., automation technologies and recommender systems). Saleema received her Ph.D. in computer science from the University of Washington’s Computer Science & Engineering department in 2012.

| Pacific Northwest National Laboratory