Artificial intelligence is intended to mimic the behavior of humans. However, AI technology can be applied in reverse – it can be used to change human behavior. There are a number of reasons AI can be great for social engineering, especially in the field of marketing.Read Full Story
Alexis Lerner, who took a couple of our courses on applied regression and communicating data and statistics, designed a new course, “Jews: By the Numbers,” at the University of Toronto:
But what does it mean to work with data and statistics in a Jewish studies course?
This note addresses the typical applied problem of estimating from data how a target “conversion rate” function varies with some available scalar score function — e.g., estimating conversion rates from some marketing campaign as a function of a targeting model score. The idea centers around estimating the integral of the rate function; differentiating this gives the rate function.Read Full Story
6-7 May, 2019 – MunichPredictive Analytics World is the leading vendor independent conference for applied machine learning for industry 4.0.Business users, decision makers and experts in predictive analytics will meet on 6-7 May 2019 in Munich to discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.Read Full Story
I presented a talk with this title at the Applied Machine Learning Conference at Tom Tom Fest in Charlottesville (which I also helped plan) last Thursday April 12, 2018.
My interest in this topic started long ago, and I partially based this talk off of my blog post “A Challenge to Data Scientists” from 2015.
An aspect that is important but often overlooked in applied machine learning is intervals for predictions, be it confidence or prediction intervals. For classification tasks, beginning practitioners quite often conflate probability with confidence: probability of 0.5 is taken to mean that we are uncertain about the prediction, while a prediction of 1.Read Full Story
In this post, I want to highlight two recent complementary results on transfer learning applied to audio — one related to music, another related to speech.
The first paper, related to music, is by Keunwoo Choi and friends, which incidentally also won the best paper award at ISMIR 2017. But that’s not the only reason why you should read it.