Driving Factors

Since 2010, the year of initiation of annual Imagenet Competition where research teams submit programs that classify and detect objects, machine learning has gained significant popularity. Machine learning, in particular deep learning, is at the moment incredibly powerful to make predictions based on large amounts of available data. Some of the amazing applications of machine learning include recommending movies based on movies one had watched in the past or recommendation of books based on books one had purchased earlier. CNN, today's most popular neural network, has achieved considerable success for image processing tasks.

Data, data, data ... Companies like Google, Facebook, Instagram, Pinterest, Amazon etc. have lots of this wealth which provides them an inherent advantage over many others in the competition. The more data that one has to train a neural network, the more training iterations it can perform, and thus can better tune the neural network before it is ready to perform. Facebook (and Instagram) can use all the photos of the billion users it has, Pinterest can use information of several billion pins that are on its site, Google can use enormous volume of search data, or Amazon can use data from the millions of products that are bought every day from its site.

Machine Learning (ML) is a fairly generic subject that, in fact, can be applied in various settings whereas Data Mining (DM) deals with utilization of data from some specific domains e.g., social media, sensor data, video streams, etc., to analyze certain relevant issues of that domain. DM may utilize ML techniques for its own purpose.

There exists a large number of professionals or academicians who have some ideas about machine learning and data mining but more interests on these topics. There are several online courses on these topics which may not be equally beneficial than listening discussions on various advanced sub-topics of these two subjects in a class or seminar room environment where interactions between participants and the speaker is an added advantage. A group of faculties and researchers of the Computer Vision and Pattern Recognition (CVPR) Unit of the Indian Statistical Institute, Kolkata who are engaged in regular teaching and research assignments will deliver lectures and also arrange a few demonstrations to enhance the knowledge and understandings on ML and DM of the target group of people from both academia and industry.