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Machine learning, a term coined by Arthur Samuel in 1959, is a field of study that originated from the pursuit of artificial intelligence^[1]. It employs techniques that allow computers to improve their performance over time through experience. This learning process often mimics the human cognitive process. Machine learning applies to various areas such as natural language processing, computer^[4] vision, and speech recognition^[3]. It also finds use in practical sectors like agriculture, medicine, and business for predictive analytics^[2]. Theoretical frameworks such as the Probably Approximately Correct learning and concepts like data mining and mathematical optimization form the foundation of machine learning. Specialized techniques include supervised and unsupervised learning, reinforcement learning, and dimensionality reduction, among others.

Terms definitions

1. ↑ artificial intelligence.

1 Artificial Intelligence (AI) refers to the field of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include reasoning, learning, planning, perception, and language understanding. AI draws from different fields including psychology, linguistics, philosophy, and neuroscience. The field is prominent in developing machine learning models and natural language processing systems. It also plays a significant role in creating virtual assistants and affective computing systems. AI applications extend across various sectors including healthcare, industry, government, and education. Despite its benefits, AI also raises ethical and societal concerns, necessitating regulatory policies. AI continues to evolve with advanced techniques such as deep learning and generative AI, offering new possibilities in various industries.

2 Artificial Intelligence, commonly known as AI, is a field of computer science dedicated to creating intelligent machines that perform tasks typically requiring human intellect. These tasks include problem-solving, recognizing speech, understanding natural language, and making decisions. AI is categorized into two types: narrow AI, which is designed to perform a specific task, like voice recognition, and general AI, which can perform any intellectual tasks a human being can do. It's a continuously evolving technology that draws from various fields including computer science, mathematics, psychology, linguistics, and neuroscience. The core concepts of AI include reasoning, knowledge representation, planning, natural language processing, and perception. AI has wide-ranging applications across numerous sectors, from healthcare and gaming to military and creativity, and its ethical considerations and challenges are pivotal to its development and implementation.

2. ↑ predictive analytics. Predictive Analytics is a field that uses a variety of statistical techniques to analyze current and historical facts to make predictions about future outcomes. It employs data modeling, machine learning, Artificial Intelligence, deep learning, and data mining to identify patterns and relationships within data. Techniques such as regression analysis, ARIMA models, time series models, and predictive modeling are key to achieving these predictions. The applications of predictive analytics are wide-ranging, from optimizing business decisions and personalizing marketing campaigns, to predicting cash flows and legal outcomes. It's an essential tool in industries like asset management, insurance, communications, and more. Moreover, its specialized applications include child protection, legal decisions, and sports analytics. Notable authors and works provide further insights into this field, which also intersects with topics like capital markets, econometric analysis, and counterterrorism.

Machine learning (Wikipedia)

Machine learning (ML) is a field of study at artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

Machine learning approaches have been applied to many fields including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.

From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning.

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