Relation between statistical machine learning and big data

While the volume, velocity, and variety of data generated worldwide rise, the number and type of techniques for processing them are soaring too.

Big data, machine learning, statistics, statistical machine learning; so many terms surfacing. What are these and how are they related is a question, the answer to which is pertinent to the effective utilization of the data that your business generates; helping you stay ahead of your peers. And what’s particularly interesting to explore is the relation between big data and statistical machine learning.

ML and statistics

Often in fields, like pattern recognition, data mining, and knowledge discovery, we see both machine learning and statistics coming together. What brings them together is a common goal – learning from data; this means both of them focus on drawing insights or knowledge from data. However, both these methods are affected by their inherent cultural differences. While statistics is a subfield of mathematics, machine learning comes from computer science and artificial intelligence. Not to forget, machine learning is a comparatively new field, made possible by the availability of cheap computing power and availability of what we call as big data that helped data scientists to train computers to learn by analyzing data. On the contrary, statistics has existed long before computers were invented.

Machine learning involves no prior assumptions w.r.t the underlying relationships between the variables. Machine learning algorithm, after it is given all the required data, processes this data and discovers patterns. You can then use these patterns on a new data set. Machine learning is generally used in case of high-dimensional data sets, wherein the more data you have, the more accurate are your predictions. However, predicting with statistics means you need to know precisely what you are doing, how the data was collected, and the underlying distribution of population you are studying.

Statistical ML

Statistics and computational sciences, such as computer science, systems science, and optimization, merge to create what is called as statistical machine learning. Statistical machine learning is marked by extremely large-scale, dynamical, and heterogeneous data streams. It is driven by applied problems in science and technology. With statistical machine learning, mathematical and algorithmic creativity is required to make this method bear results. Developments in the field of statistical machine learning heavily influence fields, such as artificial intelligence, information management, bioinformatics, communications, and signal processing.

Big data and statistical ML Both sciences and industry are facing a data revolution. And this has given rise to completely new data formats and databases of unprecedented scale. Such a rise in big data has presented an opportunity for big data and machine learning to come together and develop machine learning techniques that have the ability to handle modern data types, by drawing on statistical and computational intelligence for navigation of vast amounts of information with minimal or no human supervision. This brings us to the understanding that while big data and machine learning are not directly related, coming together of these two can do real wonders. Machines learn from the extensive calculations done over datasets. So more the data, more effective the learning.