The Good, The Bad, and The Deep Algorithms… at MLconf Seattle, May 20
MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning.
MLconf in Seattle is a week away and we are getting a glimpse of what is going to be presented. Speakers were kind enough to give us interviews about different topics in machine learning. Ethics in machine learning is the hottest conversation right now. Recently, Igor Markov, Evan Estola and Florian Tramèr answered some very sensitive and provocative questions and suggest ways to fix some of the problems in ethics in ML. Ted Wilke, an MLconf veteran, will be the keynote speaker. Apparently, his team at Intel labs had a good year in research and he is going to fill us in on their findings about the capacity of deep learning. I wonder what Intel is baking over there, let’s see if he is going to reveal to us more about their plans!
Franziska Bell, Data Science Manager at Uber, will talk about how a quantum molecular dynamic model for enzymes made Uber more reliable as a service. And of course, you don’t need to be a quantum mechanics to be a data scientist. Lucidworks’ Jake Mannix in his pre-event interview gives us some practical advice about how to build, buy, use software and what he sees as the next revolution in text search. On that practical tune, Amanda Cassari from Concur shares her experience about Scaling Global Data Science Products, Not Teams. Let’s not forget MLconf’s love for algorithms, Sam Steingold and Erin Ledell revisit some basic topics, such as multi-classification evaluation and ensemble learning. New approaches derived from their research and solution to everyday data science project. At last, Kristian Kersting (traveled all the way from Germany) along with Avi Pfeffer to present about two declarative approaches to machine learning, one through linear programming and the other through probabilistic programming. Pfefferi gave us an interview about his new book which will be displayed along with many other books from MIT-Press, CRC and other publishers.
Saturday, we’re hosting a small, Full-day Python training event from 8:00-6:00.
Mention “KDNuggets19” and save 19% on registration to MLconf Seattle and MLtrain Seattle!
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