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It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. | English |
[20] As of 2020, many sources continue to assert that machine learning remains a subfield of AI. | English |
[21][22][16] The main disagreement is whether all of ML is part of AI, as this would mean that anyone using ML could claim they are using AI. | English |
Others have the view that not all of ML is part of AI[23][24][25] where only an 'intelligent' subset of ML is part of AI. | English |
[26] The question to what is the difference between ML and AI is answered by Judea Pearl in The Book of Why. | English |
[27] Accordingly ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals. | English |
[30] Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). | English |
Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. | English |
Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. | English |
Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. | English |
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. | English |
Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). | English |
[31] The 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. | English |
Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. | English |
Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. | English |
[32] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. | English |
[33] He also suggested the term data science as a placeholder to call the overall field. | English |
[33] Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[34] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. | English |
Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. | English |
[35] A core objective of a learner is to generalize from its experience. | English |
[3][36] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. | English |
The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. | English |
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. | English |
Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. | English |
Instead, probabilistic bounds on the performance are quite common. | English |
The bias–variance decomposition is one way to quantify generalization error. | English |
For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. | English |
If the hypothesis is less complex than the function, then the model has under fitted the data. | English |
If the complexity of the model is increased in response, then the training error decreases. | English |
But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. | English |
[37] In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. | English |
In computational learning theory, a computation is considered feasible if it can be done in polynomial time. | English |
There are two kinds of time complexity results. | English |
Positive results show that a certain class of functions can be learned in polynomial time. | English |
Negative results show that certain classes cannot be learned in polynomial time. | English |
The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve. | English |
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. | English |
[38] The data is known as training data, and consists of a set of training examples. | English |
Each training example has one or more inputs and the desired output, also known as a supervisory signal. | English |
In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. | English |
Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. | English |
[39] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. | English |
An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. | English |
[13] Types of supervised learning algorithms include active learning, classification and regression. | English |
[40] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. | English |
As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. | English |
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. | English |
It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. | English |
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. | English |
The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. | English |
Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. | English |
A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. | English |
[41] Though unsupervised learning encompasses other domains involving summarizing and explaining data features. | English |
Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. | English |
Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. | English |
Other methods are based on estimated density and graph connectivity. | English |
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). | English |
Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. | English |
In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. | English |
[42] 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. | English |
Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. | English |
In machine learning, the environment is typically represented as a Markov decision process (MDP). | English |
Many reinforcement learning algorithms use dynamic programming techniques. | English |
[43] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. | English |
Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. | English |
Self-learning as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). | English |
[44] It is a learning with no external rewards and no external teacher advice. | English |
The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. | English |
The system is driven by the interaction between cognition and emotion. | English |
[45]
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: It is a system with only one input, situation s, and only one output, action (or behavior) a. | English |
There is neither a separate reinforcement input nor an advice input from the environment. | English |
The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. | English |
The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. | English |
After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations. | English |
[46] Several learning algorithms aim at discovering better representations of the inputs provided during training. | English |
[47] Classic examples include principal components analysis and cluster analysis. | English |
Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. | English |
This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. | English |
This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task. | English |
Feature learning can be either supervised or unsupervised. | English |
In supervised feature learning, features are learned using labeled input data. | English |
Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. | English |
In unsupervised feature learning, features are learned with unlabeled input data. | English |
Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization[48] and various forms of clustering. | English |
[49][50][51] Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. | English |
Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. | English |
Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. | English |
[52] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. | English |
It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data. | English |
[53] 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. | English |
However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. | English |
An alternative is to discover such features or representations thorough examination, without relying on explicit algorithms. | English |
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. | English |
The method is strongly NP-hard and difficult to solve approximately. | English |
[54] A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. | English |
Sparse dictionary learning has been applied in several contexts. | English |
In classification, the problem is to determine the class to which a previously unseen training example belongs. | English |
For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. | English |
Sparse dictionary learning has also been applied in image de-noising. | English |
The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot. | English |
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