ABOUT THE TALK

Recent years have seen a widespread adoption of machine learning in industry and academia, impacting diverse areas from advertisement to personal medicine. As more and more areas adopt machine learning and data science techniques, the question arises on how much expertise is needed to successfully apply machine learning, data science and statistics. Not every company can afford a data science team, and going your PhD in biology, no-one can expect you to have PhD-level expertise in computer science and statistics.

This talk will summarize recent progress in automating machine learning and give an overview of the tools currently available. It will also point out areas where the ecosystem needs to improve in order to allow a wider access to inference using data science techniques.

Finally we will point out some open problems regarding assumptions, and limitations of what can be automated. The talk will first describe recent process in commodification of machine learning, as witnessed by a wide array of open source packages and commercial solutions. Then I will discuss the setting of automating supervised learning, and recent progress in automatic model selection and meta learning.

Andreas Mueller

Lecturer in Data Science | Columbia University

Andreas Mueller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at New York University.

In the last four years, he has been maintainer and one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely-used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science, and democratize access to high-quality machine learning algorithms.

Andreas Mueller
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