Computer Science That Has Evolved From Pattern Recognition And Computational Learning Theory

1309 Words Oct 31st, 2015 6 Pages
‘Machine Learning is a sub discipline of Computer Science that has evolved from Pattern Recognition and Computational Learning Theory.’ ML is akin to Data Mining in the sense that both approaches look for patterns in the data set and while the former trains the program to better its understanding, the latter focuses on extraction of data for human comprehension. A typical application employing ML would involve the design and construction of an algorithm where the program is trained through huge samples of historical data to create a model. This model is later utilized on real time data sets to predict what happens next. While Machine Learning itself has been around for decades, it has found itself into reckoning with the advent of Big Data.
A naïve way to describe Big Data would be any data set that is extremely large to compute using ordinary computational power or techniques. A more popular definition would refer to the concepts of Velocity, Variety and Volume of data. Data of wide variety, structured or unstructured, that is streaming at unprecedented rates and contributing to unmanageable volume. The server logs generated for any of the popular ecommerce portals earlier had no value for they could not find their way to traditional relational databases and in individual form offered no meaning. But in aggregate these logs stored vital information ranging from the user footprints to buying patterns. MapReduce from Google, which eventually paved way for the Hadoop…
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