Notes:

Machine learning and neural networks are both related to the field of artificial intelligence (AI) and are often used together, but they are not the same thing. Machine learning is a broader field that encompasses a variety of techniques for training models to learn from data and make predictions or decisions. It includes techniques such as supervised learning, unsupervised learning, and reinforcement learning. Some examples of machine learning algorithms include decision trees, random forests, k-nearest neighbors, and support vector machines.

Neural networks, on the other hand, are a specific type of machine learning algorithm that are inspired by the structure and function of the human brain. They consist of layers of interconnected “neurons” that are trained to recognize patterns in data by adjusting the strength of the connections between them. Neural networks are particularly useful for tasks such as image recognition, speech recognition, and natural language processing.

Machine learning is a powerful tool that can be used to improve the performance of dialog systems in a variety of ways. Some examples of how machine learning is used in dialog systems include:

- Natural Language Understanding (NLU): Machine learning can be used to train a model to understand the intent and entities in a user’s input, by analyzing patterns in large amounts of labeled data. These models can then be used to interpret the user’s input and determine their intent.
- Natural Language Generation (NLG): Machine learning can be used to train a model to generate natural-sounding responses to user input. This can be done by training the model on a large dataset of human-generated responses and using it to generate responses that are similar to those in the training data.
- Dialogue Management: Machine learning can be used to train a model to manage the flow of the conversation by determining the next action or response. This can be done by training the model on a dataset of previous conversations and using it to make decisions about how to respond to a user’s input.
- Language Modeling: Machine learning can be used to train a model to predict the next word in a sentence, based on the previous words, allowing the system to generate more natural-sounding responses and to understand the user’s input more effectively.
- Personalization: Machine learning can be used to train a model to personalize the conversation with each user, based on their previous interactions and preferences.

Resources:

- ecmlpkdd.org .. european conference on machine learning and principles and practice of knowledge discovery in databases
- icml.cc .. international conference on machine learning
- ilastik.org .. interactive learning and segmentation toolkit
- jmlr.org .. journal of machine learning research
- nips.cc .. conference on neural information processing systems
- topologicalmedialab.net .. research atelier-lab at concordia university
- ukoethe.github.io/vigra .. generic programming for computer vision

Wikipedia:

See also:

Active Learning & Dialog Systems | Agent Building and Learning Environment (ABLE) | CLASSiC (Computational Learning in Adaptive Systems for Spoken Conversation) | Deep Learning & Dialog Systems | Machine Learning | Machine Learning & Chatbots | Neural Networks & Dialog Systems | PLOW (Procedure Learning on the Web) | Selectional Preference Learning

** A**

**Accuracy paradox** refers to the phenomenon where a model with high accuracy on a test dataset may not perform well on new unseen data. This can happen when the test dataset is unrepresentative of the population or when the model is overfitting the training data.

**Active learning** is a machine learning technique where the algorithm is allowed to actively query the user for labeled examples in order to improve its performance. This is particularly useful when labeled data is scarce or expensive to obtain.

**Adjusted mutual information** is a measure of the similarity between two sets of data. It is a variation of mutual information which is adjusted for chance by taking into account the probability distribution of the data.

**Algorithmic inference** refers to the process of using algorithms to infer information from data. It is a fundamental task in artificial intelligence and machine learning, and encompasses a wide range of methods and techniques, such as supervised learning, unsupervised learning, and reinforcement learning.

**Apprenticeship learning** is a machine learning technique where an agent learns to perform a task by observing and imitating a human expert. The agent is trained on a dataset of expert demonstrations and uses this information to improve its performance. This is particularly useful when the task is difficult to specify mathematically or when there is no labeled data available.

**B**

**Bag-of-words** model is a simple representation of text data where each document is represented as a bag (or unordered set) of its words, disregarding grammar and word order but keeping track of the number of occurrences of each word. This model is widely used in natural language processing and information retrieval to convert text data into a numerical format that can be used as input for machine learning algorithms.

**Base rate** is the proportion of positive examples in a dataset. It is also known as the prior probability, and it can be used to evaluate the performance of a classifier.

**Binary classification** is a type of supervised learning task where the goal is to predict one of two possible outcomes, typically labeled as positive or negative. Examples of binary classification tasks include spam detection, sentiment analysis, and disease diagnosis.

**Bongard problem** is a problem proposed by the cognitive scientist, M. Bongard, consisting of 6 sets of 12 simple geometric shapes. Each set contains 6 shapes that have a common feature, and 6 shapes that do not have this feature. The goal of the problem is to find the common feature among the shapes in each set. This problem is used to test the ability of machines and humans to recognize patterns and generalize from examples, and it is often used as a benchmark.

**C**

**Category utility** is a measure of the usefulness of a category or class in a classification task. It is typically defined as the proportion of correctly classified examples in that category, but can also include other factors such as the cost of misclassifying examples in that category or the prior probability of the category.

**CBCL** stands for Child Behavior Checklist, it is a standardized questionnaire that assesses emotional, behavioral, and social problems in children and adolescents. It is commonly used in clinical and research settings to evaluate the mental health of children.

**CellCognition** is an open-source software platform for the analysis of cell images, particularly in the field of high-throughput microscopy. The software provides a wide range of tools for image processing, segmentation, and feature extraction, as well as machine learning and visualization capabilities.

**CIML** stands for Computational Intelligence and Machine Learning, it is a field of study that deals with the use of computational methods and machine learning techniques to analyze and understand complex data, including natural language, images, sound, and other types of data. It is an interdisciplinary field that draws on techniques from computer science, statistics, and artificial intelligence, among other fields.

**Cluster analysis**, also known as clustering, is a technique used in unsupervised machine learning to group similar data points together into clusters. The goal of clustering is to divide a dataset into homogeneous groups, where the data points within each group are more similar to each other than they are to data points in other groups. There are various algorithms for clustering, such as k-means, hierarchical clustering, and density-based clustering.

**Computational learning theory** is a branch of theoretical computer science that deals with the study of the design and analysis of machine learning algorithms. It provides formal mathematical frameworks for understanding the generalization properties of machine learning models, and for analyzing the sample complexity and computational complexity of learning algorithms.

**Concept drift** refers to the gradual change in the distribution of the data over time. This can occur in problems such as online learning, where the input data changes over time, or in problems such as sensor data, where the measurements can change due to environmental factors. Concept drift can cause a machine learning model to become less accurate over time, and various methods have been proposed to detect and adapt to concept drift.

**Concept learning** is a type of machine learning task where the goal is to learn a general rule or concept that describes a set of examples. It is also known as inductive learning, and it is the process of learning from examples to make generalizations about unseen examples. Concept learning is a fundamental task in artificial intelligence and machine learning, and it is the basis for many other tasks, such as classification and prediction.

**Conditional random field** (CRF) is a type of statistical model that is often used for structured prediction tasks, such as named entity recognition and image segmentation. CRFs model the conditional probability of a sequence of output variables given a sequence of input variables, and can incorporate information about the dependencies between the output variables.

**Confusion matrix** is a table that is often used to evaluate the performance of a classification model. It shows the number of true positives, true negatives, false positives, and false negatives for a given set of data. The entries in the table are used to compute various metrics, such as accuracy, precision, and recall, that can be used to evaluate the model’s performance.

**Coupled pattern learner** is a type of machine learning model that is designed to learn patterns in data that are coupled, meaning that they are related to one another in some way. These models are often used in applications such as image analysis, where the patterns in the data are highly correlated.

**Cross-entropy method** (CEM) is an optimization algorithm that is used to find the maximum of a multi-modal function. CEM is an efficient optimization algorithm that can be used to solve complex problems, particularly when the function is noisy or has many local maxima. It works by sampling from a probability distribution that is initially centered around the current best estimate, and gradually updating the distribution based on the samples that have the highest function values.

**Cross-validation** is a technique used to evaluate the performance of a machine learning model by training the model on a portion of the data and testing it on a different portion of the data. This process is repeated multiple times with different portions of the data being used for training and testing each time. Cross-validation is used to estimate the generalization error of a model, which is the error that the model would make on new, unseen data.

**Curse of dimensionality** is a phenomenon that occurs in high-dimensional spaces, where the amount of data required to accurately estimate the underlying distribution of the data grows exponentially with the number of dimensions. This is a problem because in high-dimensional spaces, most of the data is far away from most of the other data, making it difficult to accurately estimate the underlying distribution. The curse of dimensionality can also make it difficult to find patterns in the data or to make accurate predictions.

**D**

**Data pre-processing **is a step in the data analysis process in which raw data is cleaned, transformed and made ready for analysis. This step includes tasks such as data cleaning, data transformation, data integration, and data reduction.

**Decision list **is a rule-based machine learning algorithm that uses a list of decision rules to classify instances. The decision rules are ordered and the algorithm starts at the top of the list and works its way down until a rule is found that applies to the instance in question, at which point the algorithm stops and classifies the instance accordingly.

**Decision rules **are statements of the form “IF condition THEN decision” that are used to make decisions or predictions. They are commonly used in decision tree and rule-based machine learning algorithms to classify instances.

**Decision tree learning **is a method used for both classification and regression problems in which a tree-like model of decisions and their possible consequences is built. It is a supervised learning algorithm, it recursively splits the dataset into smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes, each of which corresponds to a class label.

**Deep learning **is a subset of machine learning that uses algorithms inspired by the structure and function of the brain’s neural networks. These algorithms, called artificial neural networks, are used to analyze and model large, complex datasets. Deep learning techniques are used in a variety of applications, such as image and speech recognition, natural language processing, and decision making.

**Dimension reduction **is a technique used to reduce the number of features in a dataset while maintaining as much of the information as possible. This is done by transforming the original features into a new set of features that are a combination of the original features. Dimension reduction can be used to improve the performance of machine learning algorithms and to visualize high-dimensional data.

**Discriminative model **is a type of statistical model that tries to find the decision boundary between different classes. Discriminative models predict the class label of an input based on the input’s features, without modeling the underlying probability distribution of the data. Examples of discriminative models include logistic regression, linear discriminant analysis and support vector machines.

**Document classification **is a task of automatically categorizing a document into one or more predefined categories or topics. It is a common problem in natural language processing and information retrieval. There are several techniques used for document classification, such as using machine learning algorithms to learn from labeled training data, using a predefined set of rules, or using a combination of both.

**E**

**Eager learning** is a type of machine learning where the model is trained using all the available data, prior to making any predictions. This is in contrast to lazy learning where the model only learns from the data when a prediction is needed. Eager learning is also known as “batch learning” or “offline learning” as the model is trained with all the data before it is used to make predictions.

**Early stopping** is a technique used to prevent overfitting in machine learning models by stopping the training process before the model has a chance to fully memorize the training data. This is done by monitoring the performance of the model on a validation set and stopping the training process when the performance on the validation set starts to deteriorate.

**Elastic matching** is a technique used to match strings that are similar but not exactly the same. Elastic matching uses techniques such as edit distance and n-grams to determine the similarity between strings. It is often used in tasks such as record linkage, where the goal is to link records that refer to the same real-world entity despite differences in the way the entity is represented.

**Empirical risk minimization** is a method used in supervised learning to find the model that minimizes the expected value of the loss function on the training data. This method is used to find the model that generalizes well to unseen data. In other words, it is a method of finding the best model based on the observed data.

**Evolvability** is a measure of how easily a system can adapt and change in response to new information or changing conditions. In artificial intelligence and machine learning, evolvability refers to the ability of a model or algorithm to be modified or updated in response to new data or changing conditions.

**Expectation Propagation** is a method for approximating a probability distribution over a set of variables. Expectation Propagation is used to perform approximate inference in probabilistic graphical models. It uses a message-passing algorithm to compute approximate marginal probabilities for each variable.

**Explanation-based learning** is a type of machine learning that uses prior knowledge to improve the performance of a model. The prior knowledge is used to generate explanations for the model’s predictions, which can then be used to improve the model’s accuracy. Explanation-based learning is often used in applications where the cost of acquiring new data is high, and where the user needs to understand the reasoning behind the model’s predictions.

**F**

**Feature** is an individual measurable property or characteristic of a phenomenon being observed. Features are used as input variables for a model, and are often chosen to be relevant and informative for the task at hand. For example, in image classification, features could be the color and texture of an image.

**Feature hashing** is a technique used to represent a high-dimensional feature space as a low-dimensional one. It works by applying a hash function to the features to map them to a fixed-size feature space. The advantage of feature hashing is that it can handle large feature spaces with a relatively small memory footprint, but it has the disadvantage that it can lead to feature collisions, where different features are mapped to the same hash value.

**Feature scaling** is a technique used to standardize the range of independent variables or features of data. In machine learning, it is generally a good practice to scale the features so that they have similar ranges, as this can improve the performance of some algorithms. There are several techniques for feature scaling such as standardization, which scales the data to have a mean of 0 and a standard deviation of 1, and normalization, which scales the data to have a minimum value of 0 and a maximum value of 1.

**Feature space** is the set of all possible feature combinations for a given problem. It represents the input space of a model, where each point in the feature space corresponds to a unique set of feature values. The dimensionality of the feature space is equal to the number of features used in the problem.

**Feature vector** is an n-dimensional vector of numerical features that represent an object or pattern. Each element of the vector corresponds to a particular feature, and the entire set of values in the vector constitutes a description of the object in question. The feature vectors can be used as input to machine learning algorithms.

**Formal Concept Analysis** (FCA) is a mathematical framework for analyzing and organizing complex data sets. It is used to extract formal concepts, which are sets of objects and attributes that describe the relationships between the objects in a dataset. FCA is often used in data mining and knowledge discovery to identify patterns and regularities in large datasets and to build hierarchies of concepts that can be used for classification and clustering.

**G**

**Generative model** is a type of statistical model that is used to generate new data that is similar to the data it was trained on. Generative models learn the underlying probability distribution of the training data and can generate new samples from that distribution. Examples of generative models include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

**Granular computing** is a paradigm of computing that deals with granules, which are clusters of objects that have similar properties. Granular computing is used to extract meaningful information from large, complex data sets by breaking them down into smaller, more manageable granules. It is used in a variety of applications, such as data mining, machine learning, and natural language processing.

**Grid search** is a technique used to find the best set of hyperparameters for a machine learning model. It works by training the model on a range of hyperparameter values and evaluating the performance of the model on a validation set. The process is repeated for all combinations of the hyperparameters, and the best set of hyperparameters is selected based on the performance on the validation set. Grid search can be computationally expensive, especially for models with many hyperparameters.

**H**

**Hashing-Trick** is a technique used to convert a categorical feature to a numerical one. It works by applying a hash function to the categorical feature to map it to a fixed-size feature space. This allows the use of categorical features in machine learning algorithms that only accept numerical input. The Hashing-Trick can handle large categorical feature spaces with a relatively small memory footprint, but it has the disadvantage that it can lead to feature collisions, where different features are mapped to the same hash value.

**Hinge loss** is a loss function used in maximum-margin classification algorithms, such as support vector machines (SVMs). It is used to penalize predictions that are not confident and not correct. The hinge loss is defined as the maximum of 0 and the difference between the true label and the predicted label, multiplied by a constant. The hinge loss is used to learn a linear boundary that separates the different classes in the feature space. It is often used in binary classification problems and variants exist for multi-class classification problems.

**I**

**Inductive bias** refers to the assumptions that a learning algorithm makes about the target function or the underlying probability distribution of the data. These assumptions can limit the types of functions that the algorithm can learn, and can affect the performance of the algorithm on unseen data.

**Inductive transfer** refers to the ability of a learning algorithm to apply knowledge acquired from one task to a related but different task. It is a form of transfer learning where the model can be reused to solve a similar problem but with different characteristics

**Inferential theory of learning** is a framework for understanding how people learn from data. It posits that people use statistical inference to update their beliefs about the world based on new data. This theory proposes that people use prior knowledge and assumptions to make inferences about the underlying probability distributions that generated the data they observe.

**Instance-based learning** is a type of machine learning where the model learns by storing the training instances and comparing new instances to the stored instances. The model makes predictions based on the similarity between the new instance and the stored instances. The k-nearest neighbors algorithm is an example of instance-based learning.

**Iris flower data set** is a well-known dataset in the field of machine learning, used for classification and pattern recognition tasks. It consists of 150 instances, each described by four features: sepal length, sepal width, petal length, and petal width. The instances are labeled with one of three flower species: Iris setosa, Iris virginica, and Iris versicolor. The dataset is often used as a benchmark for testing the performance of machine learning algorithms.

**K**

**k-q-flat** is a generalization of the k-dimensional hyperplane. It is a subspace of k dimensions of a q-dimensional space. A k-q-flat is defined by a set of q-1 linear equations and is used to partition the space into different subspaces. It can be used for clustering and classification in machine learning.

**Knowledge integration** is the process of combining and integrating multiple sources of information to create a more comprehensive and accurate understanding of a problem or a phenomenon. In artificial intelligence and machine learning, knowledge integration refers to the process of combining knowledge from different sources to improve the performance of a model or an algorithm. This can be done by integrating prior knowledge with new data, or by combining multiple models or algorithms to create a more powerful one.

**L**

**Large margin nearest neighbor** (LMNN) is a supervised machine learning algorithm that tries to find a Mahalanobis distance metric that maximizes the separation between different classes while keeping the same-class samples close together. The Mahalanobis distance metric is learned using a large margin criterion similar to that used in support vector machines. LMNN is particularly useful for classification problems in which the data is not linearly separable.

**Lazy learning** is a type of machine learning where the model only learns from the data when a prediction is needed. The model does not learn from the data in advance, but instead learns incrementally as new data is encountered. This is in contrast to eager learning where the model is trained using all the available data prior to making any predictions. Lazy learning is also known as “instance-based learning” or “on-demand learning” as the model is trained on the instances when it is needed to make predictions.

**Learning automata** is a mathematical model of learning and decision making in which an agent adapts its behavior based on the outcomes of its actions. The agent uses trial-and-error learning to improve its performance over time. Learning automata are used in a variety of applications, such as control systems, robotics, and game theory.

**Learning to rank** is a method of machine learning that is used to train a model to rank items in a dataset based on their relevance to a given query. The goal of learning to rank is to optimize the order of the items in a list so that the most relevant items are presented first. This is commonly used in information retrieval and natural language processing, for example, search engines and recommendation systems.

**Learning with errors** (LWE) is a problem in cryptography where the goal is to find a secret key that is used to encrypt or decrypt a message, given a set of corrupted ciphertexts and corresponding plaintexts. LWE is a hard problem that is believed to be computationally intractable, and it is the basis for several post-quantum cryptographic systems.

**Leave-one-out error** is a method used to estimate the generalization error of a model by training it on all but one of the instances in a dataset and evaluating its performance on the instance that was left out. This process is repeated for each instance in the dataset, and the average error is used as an estimate of the model’s generalization error. This method is used to estimate the generalization error of a model in the absence of a separate validation set.

**Linear prediction function** is a function that is used to predict the value of a variable based on the values of other variables. It is a linear function of the form y = wx+b, where y is the predicted variable, x is the input variable, w is the weight and b is the bias. Linear prediction functions are used in a variety of applications, such as linear regression, time series forecasting, and signal processing.

**M**

**Margin** is a measure of the separation between the decision boundary (or hyperplane) of a classifier and the closest training instances of each class. A larger margin indicates a better separation between the classes and a classifier with a larger margin is considered to have better generalization performance.

**Matthews correlation coefficient** (MCC) is a measure of the quality of binary classification that takes into account the balance between true positives, true negatives, false positives, and false negatives. It ranges from -1 to 1, where 1 represents perfect prediction, 0 represents random prediction and -1 represents total disagreement between prediction and observation.

**Meta-learning**, also known as “learning to learn,” is a type of machine learning in which a model is trained to learn from new learning experiences. This means that the model can learn how to learn and adapt to new tasks, rather than having to be retrained from scratch for each new task. This approach is particularly useful in situations where the data distribution changes frequently.

**Mixture model** is a statistical model that represents a set of observations as a mixture of different probability distributions. It is used to model data that is composed of multiple different subpopulations. Mixture models are particularly useful for modeling data that is generated by multiple underlying sources, or for modeling data that has multiple different characteristics.

**Motor babbling** refers to the random and exploratory movements that infants make with their limbs before they learn to control them. These movements help infants develop a sense of their own body and the relationship between their movements and the environment. Motor babbling is also a technique used in robotics and machine learning to help a robot or agent learn about its own motor capabilities and the effects of its actions on the environment.

**Mountain Car problem** is a classic benchmark problem in reinforcement learning. It consists of a car trying to reach the top of a hill, however the car’s engine is not powerful enough to climb the hill directly. It must drive back and forth to build up momentum before making a final ascent. The goal is to find a control policy that allows the car to reach the top of the hill as quickly as possible.

**Multi-armed bandit** is a type of problem in reinforcement learning, in which a decision maker must choose between several different options (or “arms”), each with an unknown probability of producing a reward. The goal is to learn which arm is the best, by balancing the exploration of the different options with the exploitation of the best known option.

**Multi-task learning** is a type of machine learning in which a model is trained to perform multiple tasks simultaneously. This approach is used to improve the performance of the model on one task by leveraging the information learned from other tasks. Multi-task learning is particularly useful when there is a shared structure among the different tasks, or when the data for one task is limited.

**Multilinear principal component analysis** (MPCA) is a method for analyzing multi-way arrays, also called tensors. It is an extension of traditional principal component analysis (PCA) which is used to analyze matrices. MPCA uses a set of multilinear transformations to extract the principal components from the data.

**Multilinear subspace learning** (MSL) is a method that helps to learn a subspace from multi-way arrays. It is a generalization of traditional subspace learning methods that work with matrices. MSL uses a set of multilinear projections to extract the underlying subspace from the data.

**Multiple-instance learning** (MIL) is a type of machine learning problem where the training data consists of sets of instances, rather than individual instances. In MIL, the goal is to learn a model that can predict the label of the entire set, based on the labels of the individual instances. This approach is useful when the instances within a set are not independently and identically distributed.

**Multivariate adaptive regression splines** (MARS) is a non-parametric regression method that uses a set of basis functions to model the relationship between a set of predictor variables and a response variable. MARS adapts the complexity of the model to the complexity of the data by adjusting the number and location of the knots in the basis functions. This approach is particularly useful when the relationship between the predictor and response variables is non-linear or when there are interactions among the predictor variables.

**N**

**Nearest neighbor search** is a technique used to find the closest point(s) in a dataset to a given query point. It is used in a variety of fields, including pattern recognition, image processing, and information retrieval. The most basic form of nearest neighbor search is a linear search, where the distance between the query point and each point in the dataset is computed and the point with the smallest distance is returned. However, more efficient algorithms, such as k-d trees and locality-sensitive hashing, are often used to speed up the search.

**Neural modeling** refers to the process of using mathematical models based on artificial neural networks to represent and analyze complex systems. Neural modeling is used in a wide range of fields including computer vision, natural language processing, speech recognition, and robotics. Additionally, it is also used in fields such as cognitive science, neuroscience and psychology to model neural systems and understand their functioning.

**O**

**Offline learning**, also known as batch learning, is a type of machine learning where the model is trained using all of the available data at once, rather than incrementally as new data becomes available. This approach is used when the data is static or changes infrequently, and when the computational resources are available to process the entire dataset at once. Once the model is trained, it can be used for online prediction, which is the process of making predictions with new data.

**Overfitting** is a phenomenon that occurs when a machine learning model is trained too well on the training data and performs poorly on new, unseen data. It occurs when the model is too complex, with too many parameters, and is able to fit the noise in the training data. Overfitting can be identified by a large difference between the training and validation set performance. To avoid overfitting, techniques such as regularization and early stopping can be used.

**P**

**Parity learning** is a type of machine learning problem where the goal is to learn a function that maps a binary input vector to a binary output vector. The function should output a 1 if the number of 1s in the input vector is even and a 0 if the number of 1s in the input vector is odd. Parity learning is useful in fields such as coding theory and cryptography.

**Pattern recognition** is the process of recognizing patterns in data. It is a subfield of machine learning and artificial intelligence, and it deals with the development of algorithms and statistical models that can identify patterns in data. These patterns can be used for a wide range of applications, including image recognition, speech recognition, and natural language processing.

**Predictive learning** is a type of machine learning where the goal is to predict future outcomes based on past observations. It is also known as supervised learning, because the model is trained on labeled data, where the outcomes are known. The model learns to make predictions by finding patterns in the data that are associated with the outcomes.

**Predictive state representation** (PSR) is a method used in reinforcement learning and control systems to represent the state of a system in a way that is useful for making predictions. PSR is a compact and expressive way of representing the state of a system as a probability distribution over a set of variables. It is particularly useful when the state of the system is not fully observable and predictions need to be made based on partial information.

**Preference learning** is a type of machine learning where the goal is to learn a model that can predict the preferences of an individual or group. This is different from traditional supervised learning where the goal is to predict a particular outcome. In preference learning, the goal is to learn a model that can predict which option an individual or group would prefer, given a set of options.

**Prior knowledge** refers to any information that is known before the recognition process begins. In pattern recognition, prior knowledge can include information about the characteristics of the patterns being recognized, the context in which the patterns are found, or the relationships between the patterns. This knowledge can be used to improve the performance of the recognition process by guiding the selection of features, the design of the classifier, or the way in which the data is pre-processed.

**Proactive learning** is a type of machine learning where the model actively seeks out new information, rather than waiting for new data to be provided. The goal of proactive learning is to improve the model’s performance by seeking out the most informative data.

**Probability matching** is a strategy used in decision-making where the decision maker chooses the option with the highest probability of being the best option, as estimated by the decision maker’s prior beliefs.

**Product of experts** (PoE) is a type of ensemble model that combines the predictions of multiple simpler models. The predictions of the individual models are combined by taking the product of their predicted probabilities. PoE is particularly useful when the predictions of the individual models are not independent, and when there is a small amount of data for each model.

** R**

**Rademacher complexity** is a theoretical measure of the capacity of a learning algorithm to fit random noise in a dataset. It is defined as the expected value of the supremum of the empirical Rademacher average, which is a measure of the average classification error of a learning algorithm on a dataset. A smaller Rademacher complexity indicates that a learning algorithm has better generalization ability.

**Rand index** is a measure of the similarity between two sets of clustering results. It compares the number of pairs of instances that are either in the same cluster in both sets of results or in different clusters in both sets of results. A Rand index of 1 indicates that the two sets of results are identical, while an index of 0 indicates that they are completely different.

**Representer theorem** states that any function that can be represented as a linear combination of a set of basis functions can also be represented as a linear combination of the input-output pairs of the function. This theorem is used in machine learning to simplify the optimization problem of finding the best linear model for a given dataset.

**Rule induction** is a method of machine learning that creates a set of rules that can be used to classify new instances based on their features. The rules are usually represented in the form of “if-then” statements, and are learned from a dataset of labeled instances. Rule induction algorithms typically use decision trees or other decision-making structures to generate the rules.

**S**

**Semantic analysis** is the process of understanding the meaning of natural language text. It involves techniques such as natural language processing, machine learning, and knowledge representation to extract meaning from text. This can include identifying named entities, recognizing the intent behind a statement, or determining the relationships between different entities in the text.

**Semi-supervised learning** is a type of machine learning that combines elements of supervised and unsupervised learning. In this approach, the model is trained on a dataset that contains both labeled and unlabeled data. The goal is to leverage the information in the unlabeled data to improve the performance of the model on the labeled data. This approach is particularly useful when labeled data is expensive or difficult to obtain.

**Sequence labeling** is a type of machine learning problem where the goal is to assign a label to each element in a sequence of data. This can include tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. The model is trained on a dataset of labeled sequences, and the goal is to learn to assign the correct label to each element in a new, unseen sequence.

**Smart variables** are a concept used in probabilistic programming to represent random variables that can be changed by the program. Smart variables are used to perform computations and to make decisions that depend on the current state of the variables. They can be used to represent the current state of a robot, the beliefs of an agent, or the parameters of a model, and can be manipulated using algorithms such as Markov Chain Monte Carlo.

**Statistical classification** is a type of machine learning where the goal is to assign a label to a new instance based on a set of labeled instances. The model is trained on a dataset of labeled instances, and the goal is to learn to assign the correct label to a new, unseen instance. The most common approach to statistical classification is to use a probabilistic model, such as a Bayesian classifier, to estimate the probability of each label for a given instance.

**Statistical learning theory** is a branch of machine learning that deals with the theoretical foundations of learning from data. It provides a framework for understanding how learning algorithms work and what conditions are necessary for them to be effective. It also deals with the generalization error, which is the difference between the performance of the learned model on the training data and on unseen data.

**Statistical relational learning** (SRL) is a type of machine learning that deals with learning from data that is represented in the form of relational structures, such as graphs or logic rules. SRL approaches combine techniques from statistics, machine learning, and artificial intelligence to learn from data represented in these forms. SRL is useful in fields such as natural language processing, bioinformatics, and knowledge representation.

**Structural risk minimization** (SRM) is a principle used in machine learning to minimize the risk of overfitting by controlling the complexity of the model. SRM is based on the idea that the complexity of the model should be balanced with the amount of available data, so that the model is not too complex to fit the data. The goal of SRM is to minimize the expected risk of the model, which is a measure of its generalization ability.

**Structured learning** is a type of machine learning that deals with learning from data that is represented in a structured form, such as graphs, logical rules, or natural language text. The goal is to use the structure of the data to improve the performance of the learning algorithm. Structured learning is particularly useful when the data is complex and has a non-trivial structure, such as natural language text, biological networks, or social networks.

**Subclass reachability** is a problem in which the goal is to identify all subclasses of a given class that are reachable by following a set of relationships between classes. This problem is used in fields such as ontology engineering and knowledge representation to reason about the relationships between classes in a given ontology.

**Supervised learning** is a type of machine learning where the model is trained on a labeled dataset, where the outcomes (or labels) are known. The goal is to learn a model that can make accurate predictions about the outcome of new, unseen instances based on their features.

** T**

**Test set** is a set of data that is used to evaluate the performance of a machine learning model after it has been trained. The test set is typically set aside from the training set, which is used to train the model, and is used to evaluate the model’s ability to generalize to new, unseen data. The performance of a model on the test set is a good indicator of how well the model will perform on new, unseen data.

**Training set** is a set of data that is used to train a machine learning model. The training set is used to learn the relationships between the features of the data and the outcomes, so that the model can make accurate predictions about new, unseen data. The performance of a model on the training set is not a good indicator of its performance on new, unseen data.

**Transduction** is the process of making predictions about new, unseen instances based on the relationship between the features of the instances and the outcomes learned from a labeled dataset. In machine learning, transduction is the process of using a trained model to make predictions about new, unseen instances.

**U**

**Ugly Duckling Theorem** is a mathematical concept that states that a sequence of functions can converge uniformly to a limit function even if the limit function is not continuous at certain points.

**Uncertain data** refers to information that is incomplete, imprecise, or otherwise uncertain.

**Uniform convergence** refers to the property of a sequence of functions where the maximum difference between the functions and their limit approaches zero as the number of functions approaches infinity.

**Unsupervised learning** is a type of machine learning where the model is not provided with labeled data, and must instead find patterns or relationships within the input data on its own.

**V**

**Version space** is a concept in machine learning that refers to the set of hypotheses that are consistent with a given set of training data. It is used to represent the space of possible solutions to a learning problem.

**W**

**Ward’s method** is a method of hierarchical clustering that aims to minimize the variance of the distances between the merged clusters. This method joins the two clusters that have the minimum variance increase when they are combined. The process is repeated until the desired number of clusters is reached.