Mar 15–18, 2020

Schedule: Data Science and Machine Learning sessions

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9:00am - 5:00pm Sunday, March 15.. & Sunday, March 15
Location: 211 D
Don Fox (The Pragmatic Institute)
We will walk through all the steps - from prototyping to production - of developing a machine learning pipeline. We’ll look at data cleaning, feature engineering, model building/evaluation, and deployment. Students will extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
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9:00am12:30pm Monday, March 16, 2020
Location: LL21 E/F
Mehrnoosh Sameki (MERS) (Microsoft), Sarah Bird (Microsoft)
Main focus: Six core principles of responsible AI: fairness, reliability/safety, privacy/security, inclusiveness, transparency and accountability. We will focus on Transparency (Interpretability), Fairness, and Privacy and cover best practices and state-of-the-art open source toolkits that empower researchers, data scientists, and stakeholders to build more trustworthy AI systems. Read more.
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9:00am12:30pm Monday, March 16, 2020
Location: LL20D
Matt Harrison (MetaSnake)
We use Pandas to load data, inspect it, tweak it, visualize it, and do some analysis with only a few lines of code. We will dive into plotting, and matplotlib integration. Then we will look at data quality and issues such as missing data. Finally, we'll use the split-apply-combine paradigm with GroupBy and pivot and be familiar with stacking and unstacking data as well. Read more.
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1:30pm5:00pm Monday, March 16, 2020
Location: LL21 E/F
Patrick Hall (H2O.ai | George Washington University)
Even if you've followed current best practices for model training and assessment, machine learning models can be hacked, socially discriminatory, or just plain wrong. This presentation introduces model debugging strategies to test and fix security vulnerabilities, unwanted social biases, and latent inaccuracies in models. Read more.
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1:30pm5:00pm Monday, March 16, 2020
Location: LL21 C
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Machine learning models are data, which means they require the same data governance considerations as the rest of your data. In this tutorial we will concentrate on metadata management for model serving. We will discuss what information about running systems we need and why it is important. We will also show how Apache Atlas can be used for storing and managing this information. Read more.
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1:30pm5:00pm Monday, March 16, 2020
Location: LL20C
Robert Horton (Microsoft), Mario Inchiosa (Microsoft), John-Mark Agosta (Microsoft)
This workshop introduces the fundamental concepts of ML to business and healthcare decision makers and software product managers so that they will be able to make more effective use of machine learning results, and be better able to evaluate opportunities to apply ML in their industries. The optional exercises require a web browser and Microsoft Excel. Read more.

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