Human-centered machine learning (sponsored by H2O.ai)
Navdeep Gill takes a deep dive into how techniques from research into fair models, directly interpretable Bayesian or constrained machine learning models, and post hoc explanations can be used to train transparent, fair, and accurate models and make nearly every aspect of their behavior understandable and accountable to human users. Additional techniques from fairness research can be used to check for sociological bias in model predictions and to preprocess data and postprocess predictions to ensure the fairness of predictive models. And applying new testing and debugging techniques, often inspired by best practices in software engineering, can increase the trustworthiness of model predictions on unseen data. Together, these techniques create a new and truly human-friendly type of machine learning suitable for use in business- and life-critical decision support.
What you'll learn
- Learn how to use train human-friendly machine learning
Navdeep Gill is a software engineer and data scientist at H2O.ai, where he focuses on model interpretability, GPU-accelerated machine learning, and automated machine learning. Previously, he worked at Cisco, focusing on data science and software development, and at institutions such as California State University, East Bay; University of California, San Francisco; and Smith Kettlewell Eye Research Institute in neuroscience labs as a researcher and analyst. His work across these labs varied from behavioral, electrophysiology, and functional magnetic resonance imaging research. He earned an MS in computational statistics, a BS in statistics, and a BA in psychology with a minor in mathematics from California State University, East Bay. His interests are machine learning, time series analysis, statistical computing, data mining, and data visualization. You can find Navdeep on Twitter as @Navdeep_Gill_.
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