Machine learning is a branch of artificial intelligence that allows systems to learn from data without explicit programming. It uses algorithms to analyze data, identify patterns, and make predictions or decisions, improving their performance over time with more data.
Key words to Google
- Machine learning: “the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own“
- Deep learning: a subset of machine learning based on artificial neural networks
- Algorithm: “the hypothesis set that is taken at the beginning before the training starts with real-world data“
- Model: “a mathematical representation of a real-world process“
- Over-fitting: “a model is overfitting if it fits the training data too well and there is a poor generalization of new data“
- Under-fitting: when a model does not “learn enough patterns from the training data, and possibly [does] not even capture the dominant trend“
- Robustness: how effective your model is when being tested on new data
- Generalisation: a model that doesn’t just fit well to the training data but also to the operational data
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions.[1] Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance.[2]
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.[3][4] The application of ML to business problems is known as predictive analytics.
Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning.[6][7]
From a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning.
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[8][9] The synonym self-teaching computers was also used in this time period.[10][11]
The earliest machine learning program was introduced in the 1950s when Arthur Samuel invented a computer program that calculated the winning chance in checkers for each side, but the history of machine learning roots back to decades of human desire and effort to study human cognitive processes.[12] In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells.[13] Hebb’s model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data.[12] Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician Walter Pitts and Warren McCulloch, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes.[12]
By the early 1960s, an experimental “learning machine” with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively “trained” by a human operator/teacher to recognise patterns and equipped with a “goof” button to cause it to reevaluate incorrect decisions.[14] A representative book on research into machine learning during the 1960s was Nilsson’s book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[16] In 1981 a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17]
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”[18] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing‘s proposal in his paper “Computing Machinery and Intelligence“, in which the question “Can machines think?” is replaced with the question “Can machines do what we (as thinking entities) can do?”.[19]
