We have cars that drive themselves, phones that understand speech, and algorithms that identify objects in photos. Machine learning is the technology behind all of these intelligent-seeming products. As more varieties of data become available, the applications of machine learning will continue to proliferate. To design new products that take advantage of machine learning, designers must understand the basic principles behind this technology. In this talk, we will learn about how statistics and data science form the foundation of machine learning. We will see how every machine learning product is both enabled and constrained by the data it has access to. Finally, we will explore some new experiences that are just becoming possible as machine learning tools are maturing.
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Tony Chu
Tony is a Principal Designer at Noodle.ai, where he strives to make machine learning and data accessible to people. That means designing interfaces and visualizations that help people see patterns in data, prototyping novel data visualizations, and understand helping data scientists see the intricacies in their machine learning models.
In his spare time, he enjoys building explainers and data tools with D3js and learning about economics and statistics.
