There is no doubt in the fact that machine learning has induced revolutionary transformation in various domains. In this article, we look at those applications of machine learning which have greatly influenced the sensory domains of sight and touch. We also visit the field of statistical arbitrage as it is portrayed in various machine learning courses.
Image recognition is one of the most prominent applications of machine learning. It is used in numerous situations. The task of classifying the object into a specific category and arranging the images in terms of pixels can be easily performed with this technique. The pixel intensity is used as a parameter for classifying black and white images. When the images are colored, the intensity of three main color components i.e, red, green, and blue are taken into account.
For the recognition of images that involve face detection, several categories are formed depending upon the face of each person in the database. For the case of character recognition, a document is segmented into smaller images and each image contains a single character. We need to sort categories depending upon the letters of the English alphabet, the numerals, or some special characters as the case may be.
The field of speech recognition is gaining good prominence as far as the relationship between linguistics and computation is concerned. The translation of voice into text involves using biometric technology to identity a specific individual’s voice. The advancements in the field of voice recognition can be seen in the form of automatic speech recognition and computer speech recognition. The development of new software technologies and applications which recognize words and sentences and autocorrects them is gaining momentum.
Another important quality of speech recognition using machine learning is the segmentation of a signal into different types of words or phonemes. In this technique, a speech signal is segmented into distinct frequencies represented by various time-frequency bands.
The application domains of speech recognition include call routing, voice control systems, and chatbots operating systems.
The technique of medical diagnosis has been improvised to a large extent by the use of machine learning methods. Ranging from solving prognostic and diagnostic problems to improvement of clinical parameters, progress in medical diagnosis can be heavily attributed to machine learning techniques. Other application domains of medical diagnosis where machine learning techniques prove useful include planning, monitoring, and interpretation of data in the intensive care unit and development of intelligent alarming systems for continuous assessment.
The development of automated trading strategies by implementing a trading algorithm can be referred to as statistical arbitrage. Statistical arbitrage can be deemed as a classification rather than a clustering problem. The techniques employed in the development of such strategies include linear regression, support vector machine, and principal component analysis. With the help of these techniques, we are able to reduce the dimensions of a feature set. This gives us great insights into various trading strategies.
We conclude that the most important applications of machine learning are those which have directly influenced the sensory system of sapiens.