The different Machine Learning algorithms are grouped into a taxonomy based on their output. Some types of algorithms are:

The computational and performance analysis of machine learning algorithms is a branch of statistics known as computational learning theory.

People carry out machine learning automatically since it is such a simple process for us that we don't even realize how it is done and everything it entails. From the moment we are born until we die, human beings carry out different processes, among them we find learning through which we acquire knowledge, develop skills to analyze and evaluate through methods and techniques as well as through our own experience. However, machines must be told how to learn, since if a machine is not able to develop its skills, the learning process will not be taking place, but will only be a repetitive sequence.

Decision trees

This type of learning uses a decision tree as a predictive model. Observations about an object are mapped to conclusions about the final value of said object.

Trees are basic structures in computing. Attribute trees are the basis for decisions.

One of the two main forms of decision trees is that developed by Quinlan of measuring the entropy impurity "Entropy (information)") in each branch, something he first developed in the ID3 algorithm and then in the C4.5. Another strategy is based on the GINI index. The CART algorithm is an implementation of this strategy.[6]​.

Association rules

Association rule algorithms seek to discover interesting relationships between variables. Among the best-known methods are the a priori algorithm, the Eclat algorithm and the frequent pattern algorithm.

Genetic algorithms

Genetic algorithms are heuristic search processes that simulate natural selection. They use methods such as mutation and crossover to generate new classes that can offer a good solution to a given problem.

Artificial neural networks

Artificial neural networks (ANNs) are a machine learning paradigm inspired by neurons in animal nervous systems. It is a system of links of neurons that collaborate with each other to produce an output stimulus. The connections have numerical weights that are adapted according to experience. In this way, neural networks adapt to an impulse and are capable of learning. The importance of neural networks fell for a time with the development of support vectors and linear classifiers, but re-emerged in the late 2000s with the advent of deep learning.

Support vector machines

MVS are a series of supervised learning methods used for classification and regression. MVS algorithms use a set of training examples classified into two categories to build a model that predicts whether a new example belongs to one or the other of those categories.

Clustering algorithms

Clustering analysis is the classification of observations into subgroups —clusters— so that the observations in each group are similar to each other according to certain criteria.

Clustering techniques make different inferences about the structure of the data; They are usually guided by a specific similarity measure and by a level of internal compactness (similarity between members of a group) and separation between different groups.

Clustering is an unsupervised learning method and is a very popular statistical data analysis technique.

Bayesian networks

A Bayesian network, belief network or directed acyclic model is a probabilistic model that represents a series of random variables and their conditional independence through a directed acyclic graph. A Bayesian network can represent, for example, probabilistic relationships between diseases and symptoms. Given certain symptoms, the network can be used to calculate the probabilities that certain diseases are present in an organism. There are efficient algorithms that infer and learn using this type of representation.

In machine learning we can obtain 3 types of knowledge, which are:

All three types are carried out during a machine learning process, but the importance of each type of knowledge depends on the characteristics of what you are trying to learn.

Learning is more than a necessity, it is a primary factor in meeting the needs of artificial intelligence.

Supervised learning is characterized by having information that specifies which data sets are satisfactory for the learning objective. An example could be software that recognizes whether or not a given image is an image of a face: to learn the program we would have to provide it with different images, specifying in the process whether or not they are faces.

In unsupervised learning, on the other hand, the program does not have data that defines what information is satisfactory or not. The main objective of these programs is usually to find patterns that allow data to be separated and classified into different groups, based on their attributes. Following the previous example, unsupervised learning software would not be able to tell us if a given image is a face or not, but it could, for example, classify images between those that contain human faces, animal faces, or those that do not. The information obtained by an unsupervised learning algorithm must subsequently be interpreted by a person to make it useful.

• - Search engines.

• - Medical diagnosis.

• - Detection of fraud with the use of credit cards.

• - Analysis of the stock market.

• - Classification of DNA sequences.

• - Feature engineering.

• - Speech recognition.

• - Robotics.

• - Data mining.

• - Big Data.

• - Time series forecasts").

Below are a series of topics that could be part of the syllabus of a course on machine learning.

• - Modeling of conditional probability density functions: classification and regression

• Artificial neural networks

• Decision trees: Decision tree learning uses a decision tree as a predictive model that maps observations to conclusions about the value of a given object.

• Multiple regression models not postulated

• Regression in Gaussian processes

• Linear discriminant analysis

• k-nearest neighbors

• Perceptron

• Radial basis functions

• Support vector machines.

• - Modeling of probability density functions using generative models

*EM algorithm

• Graphical models, such as Bayesian networks and Markov random fields

• Generative topographic mapping").

• - Approximate inference techniques

• Markov Chains and Monte Carlo Method

• Variational methods.

• - Optimization: Most of the methods described above use optimization algorithms or are themselves instances of optimization problems.

Machine learning was born from the search for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers became interested in making machines learn. They tried to solve the problem with various symbolic methods, as well as what they called 'neural networks', which were generally perceptrons and other models basically based on generalized linear models as known in statistics.

Since the 2010s, advances in both machine learning algorithms and computing hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of nonlinear hidden units.[7] By 2019, graphical processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method for training large-scale commercial cloud AI.[8] OpenAI estimated hardware computing. used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of computation required, with a doubling time trend line of 3.4 months.[9]​[10]​.

Machine learning algorithms can often be affected by bias in the data (See algorithmic bias). For example, not all entries for which no information was received in the training phase can be classified. In fact, when training is carried out with data classified by humans, machine learning tends to create the same biases that exist in society. Some examples of this are when in 2015 the Google Photos algorithm identified some black people as gorillas or in 2016 when Microsoft's Twitter bot developed racist and sexist behaviors based on observing data traffic on said social network. For this reason, in recent years there has been a tendency to develop methods to increase fairness "Fairness (machine learning)"), that is, to reduce the bias in this type of algorithms by AI experts. Quoting Fei-fei Li "There is nothing special about AI. It is inspired by people, it is created by people, and most importantly it impacts people. It is a very powerful tool that we have only begun to understand, and that is a great responsibility" [12]​.

• - Adversarial machine learning.

• - Deep learning.

• - System dynamics.

• - Artificial intelligence.

• - Computational intelligence.

• - Internet of things.

• - Dynamic system.

• - Pattern recognition.

• - Association rules.

• - Autonomous robot.

• - Fairness (machine learning) "Fairness (machine learning)").

• - Ablation (artificial intelligence) "Ablation (artificial intelligence)").

• - OpenAI Codex.

• - Fawkes (image cloaking software) "Fawkes (image cloaking software)".

• - Residual neural network.

• - Hyperparameter (machine learning) "Hyperparameter (machine learning)").

• - Learning through temporal differences.

• - Learning by sets.

• - Rule-based machine learning.

• - Statistical learning theory.

• - Bias-variance dilemma.

• - Ockham Learning.

• - Bishop, Christopher (2008) Pattern Recognition and Machine Learning. Springer Verlag. ISBN=978-0-3873-1073-2.

• - Flach, Peter (2012) Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press. ISBN 978-1-107-42222-3.

• - Gollapudi, Sunila (2016) Practical Machine Learning. Packt Publishing. ISBN=978-1-78439-968-4.

• - Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664 pages, ISBN 978-0-12-374856-0.

• - Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.

• - Raschka, Sebastian (2015). Python Machine Learning, Packt Open Source. ISBN 978-1-78355-513-0.

• - Practical examples of Machine Learning in Spanish.

• - Blog about Machine Learning - The bible of Machine Learning.

• - Machine Learning will change the world.

• - Machine Learning Development with Perl (in English).

• - Study and application of machine learning techniques oriented to the medical field: estimation and explanation of individual predictions. Autonomous University of Madrid.

• - AlphaGo Archived February 4, 2018 at the Wayback Machine.

• - Machine Learning explained (podcast).

• - Machine Learning: Selection of classification metrics (in Spanish).

The different Machine Learning algorithms are grouped into a taxonomy based on their output. Some types of algorithms are:

The computational and performance analysis of machine learning algorithms is a branch of statistics known as computational learning theory.

People carry out machine learning automatically since it is such a simple process for us that we don't even realize how it is done and everything it entails. From the moment we are born until we die, human beings carry out different processes, among them we find learning through which we acquire knowledge, develop skills to analyze and evaluate through methods and techniques as well as through our own experience. However, machines must be told how to learn, since if a machine is not able to develop its skills, the learning process will not be taking place, but will only be a repetitive sequence.

Decision trees

This type of learning uses a decision tree as a predictive model. Observations about an object are mapped to conclusions about the final value of said object.

Trees are basic structures in computing. Attribute trees are the basis for decisions.

One of the two main forms of decision trees is that developed by Quinlan of measuring the entropy impurity "Entropy (information)") in each branch, something he first developed in the ID3 algorithm and then in the C4.5. Another strategy is based on the GINI index. The CART algorithm is an implementation of this strategy.[6]​.

Association rules

Association rule algorithms seek to discover interesting relationships between variables. Among the best-known methods are the a priori algorithm, the Eclat algorithm and the frequent pattern algorithm.

Genetic algorithms

Genetic algorithms are heuristic search processes that simulate natural selection. They use methods such as mutation and crossover to generate new classes that can offer a good solution to a given problem.

Artificial neural networks

Artificial neural networks (ANNs) are a machine learning paradigm inspired by neurons in animal nervous systems. It is a system of links of neurons that collaborate with each other to produce an output stimulus. The connections have numerical weights that are adapted according to experience. In this way, neural networks adapt to an impulse and are capable of learning. The importance of neural networks fell for a time with the development of support vectors and linear classifiers, but re-emerged in the late 2000s with the advent of deep learning.

Support vector machines

MVS are a series of supervised learning methods used for classification and regression. MVS algorithms use a set of training examples classified into two categories to build a model that predicts whether a new example belongs to one or the other of those categories.

Clustering algorithms

Clustering analysis is the classification of observations into subgroups —clusters— so that the observations in each group are similar to each other according to certain criteria.

Clustering techniques make different inferences about the structure of the data; They are usually guided by a specific similarity measure and by a level of internal compactness (similarity between members of a group) and separation between different groups.

Clustering is an unsupervised learning method and is a very popular statistical data analysis technique.

Bayesian networks

A Bayesian network, belief network or directed acyclic model is a probabilistic model that represents a series of random variables and their conditional independence through a directed acyclic graph. A Bayesian network can represent, for example, probabilistic relationships between diseases and symptoms. Given certain symptoms, the network can be used to calculate the probabilities that certain diseases are present in an organism. There are efficient algorithms that infer and learn using this type of representation.

In machine learning we can obtain 3 types of knowledge, which are:

All three types are carried out during a machine learning process, but the importance of each type of knowledge depends on the characteristics of what you are trying to learn.

Learning is more than a necessity, it is a primary factor in meeting the needs of artificial intelligence.

Supervised learning is characterized by having information that specifies which data sets are satisfactory for the learning objective. An example could be software that recognizes whether or not a given image is an image of a face: to learn the program we would have to provide it with different images, specifying in the process whether or not they are faces.

In unsupervised learning, on the other hand, the program does not have data that defines what information is satisfactory or not. The main objective of these programs is usually to find patterns that allow data to be separated and classified into different groups, based on their attributes. Following the previous example, unsupervised learning software would not be able to tell us if a given image is a face or not, but it could, for example, classify images between those that contain human faces, animal faces, or those that do not. The information obtained by an unsupervised learning algorithm must subsequently be interpreted by a person to make it useful.

• - Search engines.

• - Medical diagnosis.

• - Detection of fraud with the use of credit cards.

• - Analysis of the stock market.

• - Classification of DNA sequences.

• - Feature engineering.

• - Speech recognition.

• - Robotics.

• - Data mining.

• - Big Data.

• - Time series forecasts").

Below are a series of topics that could be part of the syllabus of a course on machine learning.

• - Modeling of conditional probability density functions: classification and regression

• Artificial neural networks

• Decision trees: Decision tree learning uses a decision tree as a predictive model that maps observations to conclusions about the value of a given object.

• Multiple regression models not postulated

• Regression in Gaussian processes

• Linear discriminant analysis

• k-nearest neighbors

• Perceptron

• Radial basis functions

• Support vector machines.

• - Modeling of probability density functions using generative models

*EM algorithm

• Graphical models, such as Bayesian networks and Markov random fields

• Generative topographic mapping").

• - Approximate inference techniques

• Markov Chains and Monte Carlo Method

• Variational methods.

• - Optimization: Most of the methods described above use optimization algorithms or are themselves instances of optimization problems.

Machine learning was born from the search for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers became interested in making machines learn. They tried to solve the problem with various symbolic methods, as well as what they called 'neural networks', which were generally perceptrons and other models basically based on generalized linear models as known in statistics.

Since the 2010s, advances in both machine learning algorithms and computing hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of nonlinear hidden units.[7] By 2019, graphical processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method for training large-scale commercial cloud AI.[8] OpenAI estimated hardware computing. used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of computation required, with a doubling time trend line of 3.4 months.[9]​[10]​.

Machine learning algorithms can often be affected by bias in the data (See algorithmic bias). For example, not all entries for which no information was received in the training phase can be classified. In fact, when training is carried out with data classified by humans, machine learning tends to create the same biases that exist in society. Some examples of this are when in 2015 the Google Photos algorithm identified some black people as gorillas or in 2016 when Microsoft's Twitter bot developed racist and sexist behaviors based on observing data traffic on said social network. For this reason, in recent years there has been a tendency to develop methods to increase fairness "Fairness (machine learning)"), that is, to reduce the bias in this type of algorithms by AI experts. Quoting Fei-fei Li "There is nothing special about AI. It is inspired by people, it is created by people, and most importantly it impacts people. It is a very powerful tool that we have only begun to understand, and that is a great responsibility" [12]​.

• - Adversarial machine learning.

• - Deep learning.

• - System dynamics.

• - Artificial intelligence.

• - Computational intelligence.

• - Internet of things.

• - Dynamic system.

• - Pattern recognition.

• - Association rules.

• - Autonomous robot.

• - Fairness (machine learning) "Fairness (machine learning)").

• - Ablation (artificial intelligence) "Ablation (artificial intelligence)").

• - OpenAI Codex.

• - Fawkes (image cloaking software) "Fawkes (image cloaking software)".

• - Residual neural network.

• - Hyperparameter (machine learning) "Hyperparameter (machine learning)").

• - Learning through temporal differences.

• - Learning by sets.

• - Rule-based machine learning.

• - Statistical learning theory.

• - Bias-variance dilemma.

• - Ockham Learning.

• - Bishop, Christopher (2008) Pattern Recognition and Machine Learning. Springer Verlag. ISBN=978-0-3873-1073-2.

• - Flach, Peter (2012) Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press. ISBN 978-1-107-42222-3.

• - Gollapudi, Sunila (2016) Practical Machine Learning. Packt Publishing. ISBN=978-1-78439-968-4.

• - Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664 pages, ISBN 978-0-12-374856-0.

• - Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.

• - Raschka, Sebastian (2015). Python Machine Learning, Packt Open Source. ISBN 978-1-78355-513-0.

• - Practical examples of Machine Learning in Spanish.

• - Blog about Machine Learning - The bible of Machine Learning.

• - Machine Learning will change the world.

• - Machine Learning Development with Perl (in English).

• - Study and application of machine learning techniques oriented to the medical field: estimation and explanation of individual predictions. Autonomous University of Madrid.

• - AlphaGo Archived February 4, 2018 at the Wayback Machine.

• - Machine Learning explained (podcast).

• - Machine Learning: Selection of classification metrics (in Spanish).