SAU
Department of Computer Science

Machine Learning

Machine Learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.

Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are:

  • Supervised learning. The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end.
  • Between supervised and unsupervised learning is semi-supervised learning, where the expert gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle were the entire set of problem instances is known at learning time, except that part of the targets are missing.

We are currently working on the semi-supervised framework and its application in general to recognition and segmentation of images. In general, automatic recognition system requires extraction of robust features from the face images in the first step. Then the classification of these images is done by using machine learning tools on the extracted features. In the process we are trying to propose extension of Twin Support Vector Machines for segmentation, recognition, regression, clustering and classification.

Area Contact: Dr Reshma Khemchandani

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