Machine Learning

Machine learning (ML) is the study of computer algorithms that automatically improve through the use of experience and data. It is seen as a part of artificial intelligence. Machine learning algorithms create a model based on sample data, known as "training data", to do so without making explicit predictive programming or forecasting decisions. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, and computer vision, where it is difficult or inefficient to develop traditional algorithms to perform essential tasks.

A 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. The study of mathematical optimization saves methods, theory, and application domains in the field of machine learning. Data mining is a related field of study, with a focus on exploratory data analysis through unexplained learning. In its application in business problems, machine learning is also called predictive analysis.

Overview

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its algorithm, rather than having human programmers specify every needed step.

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.

Data Mining

Machine learning and data mining often employ similar methods and overlap, but focus on machine learning prediction, based on properties learned from training data, attention to the discovery of (previously) unknown properties in data mining data Focuses (it is) the analysis step of knowledge discovery in the database). Data mining uses many machine learning methods, but with different goals; On the other hand, machine learning also employs data mining methods as "uncertain learning" or as a preprocessor step to improve learning accuracy. The confusion between these two research communities (which often take place at different conferences and different journals, ECML PKDD is a major exception) comes from the basic assumptions they work with: machine learning, performance is usually evaluated concerning capacity. Reproduces known knowledge, while important work in knowledge discovery and data mining (KDD) is the discovery of previously unknown knowledge. Evaluated for known knowledge, an uninfected (unheard) method will easily outperform other supervised methods, whereas, in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Statistics

There are closely related fields in terms of machine learning and statistical methods, but differ in their major goal: statistics estimate a population from a sample, while machine learning provides general estimating patterns. Michael I. According to Jordan, machine learning has a long history in statistics, from methodological principles to theoretical tools. He suggested the term data science as a placeholder to call the overall field.

Leo Breiman distinguished two statistical modeling paradigms: the data model and the algorithmic model, in which the "algorithmic model" refers to random machine learning algorithms such as random forests.

Some statisticians have adopted methods from machine learning, creating a joint field they call statistical education.

Optimization

There are also interrelations for the optimization of machine learning: many training problems are formulated as the minimization of some loss function on an example training set. Loss functions express the discrepancy between predictions of the model being trained and actual problem instances (for example, in classification, one wants to assign a label to an instance, and a set of pre-determined labels in the model Trained to make. Correct predictions. Example)

Generalization

The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can reduce losses on a training set, machine learning is concerned with minimizing losses on unseen samples. The characterization of the generalization of various learning algorithms is an active topic of current research, particularly for intensive learning algorithms.

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