Pattern recognition

Statistical modeling of data, Naïve Bayes’ methods, techniques of parametric and non-parametric estimation, supervised classifiers such as hidden markov models, linear discriminant functions and non-linear discriminant functions including neural networks and support vector machines, unsupervised classifiers (various clustering techniques), hybrid classifiers (or semi-supervised learning) and stochastic learning.

Image and video processing and analysis

Enhancement of images in the space and frequency domain, histogram processing of images, various segmentation techniques, object localization in video frames/ images, classification of video frames, content-based video indexing.

Natural language processing

Tokenization, parts-of-speech tagging, creating dictionaries and searching through them, automated summary generation, processing of natural language text and organizing the output for information retrieval.

Data Mining

Rule-based classifiers, association mining, generation of and searching through frequent pattern trees, generation of association rules, sequential pattern mining of data.