Machine learning has become a key technique for solving problems in computational finance, for credit scoring and algorithmic trading; in image processing and computer vision, for face recognition, motion detection, and object detection; in computational biology, for tumor detection, drug discovery, and DNA sequencing; in energy production, for price and load forecasting; in automotive, aerospace, and manufacturing, for predictive maintenance; and in natural language processing, for voice recognition applications.
Machine learning (ML) is the study of algorithms and statistical models that computer systems use to perform a task without using explicit instructions, relying on models and inference instead. Huh? Machine learning is a technique that teaches computers to do what comes naturally to humans and animals: learn from “experience”. Machine learning algorithms use computational methods to “learn” information from data without relying on a predetermined equation as a model. Huh? It “sees” the input, it “sees” the associated output, and based on computational methods, builds the equation/model as it were, with which it can then predict outcomes for new inputs. The algorithms adaptively improve their performance as the number of samples available for learning increases.
Two types of computational methods are used:
Deep learning is a specialized form of machine learning in which a computer model learns to do classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labelled data and neural network architectures that contain many layers.