38 Facts About Machine learning

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning.

Discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.

Term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.

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Machine learning, reorganized as a separate field, started to flourish in the 1990s.

Machine learning has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples.

Difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

Machine learning suggested the term data science as a placeholder to call the overall field.

Computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning model.

Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:.

Types of supervised-Machine learning algorithms include active Machine learning, classification and regression.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.

Unsupervised Machine learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points.

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Reinforcement Machine learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible.

Reinforcement Machine learning algorithms are used in autonomous vehicles or in Machine learning to play a game against a human opponent.

Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array .

The CAA self-Machine learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions about consequence situations.

Feature Machine learning algorithms, called representation Machine learning algorithms, often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions.

Deep Machine learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of lower-level features.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Sparse dictionary Machine learning is a feature Machine learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix.

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge.

The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.

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Inductive logic programming is an approach to rule-Machine learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses.

Decision tree Machine learning uses a decision tree as a predictive model to go from observations about an item to conclusions about the item's target value .

Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.

Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions.

Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.

Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server.

In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.

In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.

Machine learning is recently applied to predict the green behavior of human-being.

Recently, machine learning technology is applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone.

Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature.

When trained on man-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.

Machine learning systems used for criminal risk assessment have been found to be biased against black people.