Mar 15–18, 2020

Schedule: AI Focus: Model Creation & Tuning sessions

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11:00am11:40am Tuesday, March 17, 2020
Location: 210 C/G
Hannes Hapke (SAP ConcurLabs), Catherine Nelson (Concur Labs, SAP Concur)
Measuring your machine learning model’s performance is key for every successful data science project. Therefore, model feedback loops are essential to capture feedback from users and expand your model’s training dataset. Hannes Hapke and Catherine Nelson explore the concept of model feedback and guide you through a framework for increasing the ROI of your data science project. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: 210 C/G
Sumeet Vij (Booz Allen Hamilton)
Weak supervision allows the use of noisy sources to provide supervision signals for labeling large amounts of training data. Sumeet Vij showcases an approach combining a Snorkel weak supervision framework with denoising labeling functions, a generative model, and AI-powered search to train classifiers leveraging enterprise knowledge, without the need for tens of thousands of hand-labeled examples. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: 210 C/G
Carlos Pazos (SparkCognition), Keith Moore (SparkCognition)
AutoML brings acceleration and democratization of data science, but in the game of accuracy and flexibility, using predefined blueprints to find adequate algorithms falls short. Carlos Pazos and Keith Moore shine a spotlight on a neuroevolutionary approach to AutoML to custom build novel, sophisticated neural networks that perfectly represent the relationships in your dataset. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: 210 C/G
Navdeep Gill (H2O.ai)
Like all good software, machine learning models should be debugged to discover and remediate errors. Navdeep Gill explores several standard techniques in the context of model debugging—disparate impact, residual, and sensitivity analysis—and introduces novel applications such as global and local explanation of model residuals. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: 210 C/G
Ben Fowler (Southeast Toyota Finance)
Selecting the optimal set of features is a key step in the machine learning modeling process. Ben Fowler shares research that tested five approaches for feature selection. The approaches included current widely used methods, along with novel approaches for feature selection using open source libraries, building a classification model using the Lending Club dataset. Read more.
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5:05pm5:45pm Tuesday, March 17, 2020
Location: 210 C/G
Subutai Ahmad (Numenta)
Given that today's machine learning systems can't come close to the flexibility and generality of the brain, it's normal to ask how we can learn from the brain to improve them. Sparsity provides a great starting point. Subutai Ahmad explains how sparsity works in the brain and how applying sparsity to artificial neural networks provides significant advantages. Read more.

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