Artificial Intelligence, Machine Learning, Deep Learning and The Math  

Artificial Intelligence (AI) has been widely used in diverse domains in industry and academia. Artificial Intelligence (AI) is the field of computer science that focuses on developing intelligent machines that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Machine learning (ML) is a subset of AI that involves the development of algorithms and statistical models that allow machines to automatically learn from data and improve their performance over time without being explicitly programmed. ML can be supervised, unsupervised, or semi-supervised, depending on the availability of labeled training data.

Deep learning (DL) is a subfield of ML that involves the development of artificial neural networks, specifically Convolutional Neural Networks with multiple layers, which can automatically learn hierarchical representations of the input data. DL has revolutionized several areas of AI, such as computer vision, natural language processing, and speech recognition.

Artificial Intelligence introduces either supervised or unsupervised agents to collect information from the environment, check the other conditional parameters and provide the output decisions. Whereas, ML asks the same information from a pre-existing dataset(s) either labelled or unlabeled, and therefore responsible to extract the features from the dataset(s), find out the correct pattern set (vectors) of different classes and assists in making correct predictive decisions. The deep learning requires to preprocess the raw datasets first, secondly extract features from it, and thereafter create a pattern to match up with the expected outcome. In our study we focused on how to extract unique features from pre-existing datasets. Mathematics plays a crucial role in finding the unique features. Those unique features, once identified and filtered, are able to contribute towards pattern recognition and accurate decision making.