Schedule: Expo Hall sessions

Understanding decisions made by machine learning systems is critical for sensitive uses, ensuring fairness, and debugging production models. Interpretability presents options for trying to understand model decisions. Harsha Nori, Sameul Jenkins, and Rich Caruana explore the tools Microsoft is releasing to help you train powerful, interpretable models and interpret existing black box systems. Read more.

Recruiting patients for clinical trials is a major challenge in drug development. Saif Addin Ellafi and Scott Hoch explain how Deep 6 uses Spark NLP to scale its training and inference pipelines to millions of patients while achieving state-of-the-art accuracy. They dive into the technical challenges, the architecture of the full solution, and the lessons the company learned. Read more.

In an era where discussions among data scientists are monopolized by the latest trends in machine learning, the role of semantics in data science is often underplayed. Panos Alexopoulos presents real-world cases where making fine, seemingly pedantic, distinctions in the meaning of data science tasks and the related data has helped improve significantly the effectiveness and value. Read more.

Gerard de Melo takes a deep dive into the kinds of sentiment and emotion consumers associate with a text. With new data-driven approaches, organizations can better pay attention to what's being said about them in different markets. And you can consider fonts and palettes best suited to convey specific emotions, so organizations can make informed choices when presenting information to consumers. Read more.

Eventbrite is exploring a new machine learning approach that allows it to harvest data from customer search logs and automatically tag events based upon their content. John Berryman dives into the results and how they have allowed the company to provide users with a better inventory-browsing experience. Read more.

Mansplaining. Know it? Hate it? Want to make it go away? Sireesha Muppala, Shelbee Eigenbrode, and Emily Webber tackle the problem of men talking over or down to women and its impact on career progression for women. They also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds, examine ownership of the problem for women and men, and suggest helpful strategies. Read more.

Automating decisions require a system to consider more than just a data-driven prediction. Real-world decisions require additional constraints and fuzzy objectives to ensure they're robust and consistent with business goals. Brian Keng takes a deep dive into how to leverage modern machine learning methods and traditional mathematical optimization techniques for decision automation. Read more.

Recent advances in machine learning frameworks for the browser such as TensorFlow provides the opportunity to craft truly novel experiences within frontend applications. Victor Dibia explores the state of the art for machine learning in the browser using TensorFlow and outlines its use in the design of Handtrack.js—a library for prototyping real-time hand detection in the browser. Read more.

Heitor Murilo Gomes and Albert Bifet introduce you to a machine learning pipeline for streaming data using the streamDM framework. You'll also learn how to use streamDM for supervised and unsupervised learning tasks, see examples of online preprocessing methods, and discover how to expand the framework by adding new learning algorithms or preprocessing methods. Read more.