Interactive learning - (Daniel Hsu)
Daniel Hsu is an assistant professor in the Department of Computer Science (http://www.cs.columbia.edu/) and a member of the Data Science Institute (http://datascience.columbia.edu/), both at Columbia University. Previously, he was a postdoc at Microsoft Research New England (http://research.microsoft.com/en-us/labs/newengland/default.aspx), and the Departments of Statistics at Rutgers University (http://statistics.rutgers.edu/) and the University of Pennsylvania (http://statistics.wharton.upenn.edu/). He holds a Ph.D. in Computer Science from UC San Diego (http://cse.ucsd.edu/), and a B.S. in Computer Science and Engineering from UC Berkeley (http://www.eecs.berkeley.edu/). He received a 2014 Yahoo ACE Award (http://yahooresearch.tumblr.com/post/98987099376/2014-yahoo-ace-award-recipients-selected), was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch" in 2015, and received a 2016 Sloan Research Fellowship (http://www.sloan.org/sloan-research-fellowships/2016-sloan-research-fellows/).
Daniel's research interests are in algorithmic statistics, machine learning, and privacy. His work has produced the first computationally efficient algorithms for numerous statistical estimation tasks (including many involving latent variable models such as mixture models, hidden Markov models, and topic models), provided new algorithmic frameworks for tackling interactive machine learning problems, and led to the creation of highly-scalable tools for machine learning applications.
Commodity Machine Learning - (Andreas Mueller)
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 getting 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.
Andreas is an Research Engineer at the NYU Center for Data Science, building open source software for data science. Previously, he worked as a Machine Learning Scientist at Amazon, developing solutions for computer vision and forecasting problems. He is one of the core developers of the scikit-learn machine learning library, and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.
Social event after:
The Storehouse - 69 W 23rd St.
See you at the event!
- Rizwan, Maryam, Kishan, Matt & Max
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