&amp;quot;The future is here. It's just not evenly distributed yet.&amp;quot; - William Gibson :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
As sports have become high stakes, global competitions, the performance margins that differentiate good,great and legendary have shrunk dramatically. The importance of finding tiny advantages is greater than ever. Where Moneyball was once a novelty, now it is the norm in every sport as data is mined to find those vanishingly small advantages. And yet, even as sports are awash in data, much of it is applied to surprisingly little effect. David Epstein will discuss the importance of combining big data with cutting edge science about expertise to emerge with "small data": the kind that reveals where those tiny advantages are hiding. Read more at http://library.fora.tv/2015/06/10/05_the_margin_between_good_and_great_and_how_to_find_it#XucuJ30Lg9HhBgVB.99
Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.
The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find patterns in large-scale time-series sequences.
We review the state of the art in four related fields: (1) similarity search and pattern discovery, (2) linear modeling and summarization, (3) non-linear modeling and forecasting, and (4) the extension of time-series mining and tensor analysis. The emphasis of the tutorial is to provide the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to introduce case studies that illustrate their practical use.
In Japanese version of this blog, I've written a series of posts about how each kind of machine learning classifiers draws various classification hyperplanes or decision boundaries. So in this post I want to show you a summary of the series and how their hyperplanes or decision boundaries vary
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