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Saturday, October 20 • 1:00pm - 1:30pm
Practical Aspects of Machine Learning Models

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Deep learning techniques are rapidly gaining popularity in a variety of industries. Developing accurate forecasting models can guarantee success in a corporate environment. Specifically, accurate demand prediction can empower decision making and expand vital planning capabilities of a company. These models can allow better negotiation on flexible shipping rates, as well as avoiding premium fees.
This talk will go over an industry use case that focuses on forecasting using both classical and modern machine learning techniques. The practical aspects of model building process will be discussed: utilizing the CRISP-DM approach and communicating results at the executive level.

avatar for Inga Maslova

Inga Maslova

Professor, University of Southern California
Inga Maslova is a data scientist specializing in predictive analytics, machine learning, big data, and applications to finance, business analytics, economics, hydrology, remote sensing, precision agriculture, and climate change problems. She is currently teaching at Marshall School... Read More →

Saturday October 20, 2018 1:00pm - 1:30pm
Ballroom # 408A