Machine Learning

Extracted Attributes

• Foundations

  Statistics, mathematical optimization, and computer science

• Primary Goal

  To develop algorithms that learn from data and generalize to unseen data

• Field of Study

  Artificial Intelligence

• Origin of Term

  Coined by Arthur Samuel in 1959

• Corporate Nomenclature

  Used as the approved term for early AI at Google Ventures

Timeline

• Arthur Samuel coins the term 'Machine Learning' and publishes his landmark paper on checkers in the IBM Journal. (Source: IBM / MIT Sloan)

  1959-01-01

• Inception of the Machine Learning journal. (Source: Wikidata)

  1986-01-01

• Machine learning begins to flourish as an independent field, shifting focus from symbolic AI to statistical and probabilistic methods. (Source: Wikipedia)

  1990-01-01

• Bill Maris co-founds Google Ventures, heavily leveraging machine learning and data to drive portfolio investment strategies. (Source: Document dfb3416b-b201-4556-acf5-a96bd6e1029d)

  2009-01-01

Machine learning

Machine learning (ML) is a field of study in 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 being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical viewpoint, probably approximately correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms can be described as empirical risk minimisation under this framework.

Web Search Results

• Machine learning - Wikipedia

  Glossary Glossary v t e Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from pre-trained data and generalize to unseen data, and thus perform tasks "Task (computing)") without being explicitly programmed.( Advances in the field of deep learning have allowed neural networks "Neural network (machine learning)"), a class of statistical algorithms, to surpass many previous machine learning approaches in performance. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.( [...] The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the probably approximately correct learning model._[clarification needed_] Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalisation error._[citation needed_] [...] Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.( ### Data compression [edit] This section is an excerpt from Data compression § Machine learning.[edit]

• What is Machine Learning? - Google for Developers

  Spark icon ## Page Summary Machine learning (ML) is a way to train software, called a model, to make predictions or generate content using data. ML systems can be categorized as supervised, unsupervised, reinforcement, or generative AI, each learning differently. Supervised learning uses labeled data to make predictions, often for regression (predicting numerical values) or classification (categorizing data). Unsupervised learning identifies patterns in unlabeled data, commonly using clustering to group similar data points. Generative AI creates new content, such as text, images, or music, by learning patterns from existing data and mimicking them. [...] Machine learning (ML) powers some of the most important technologies we use, from translation apps to autonomous vehicles. This course explains the core concepts behind ML. ML offers a new way to solve problems, answer complex questions, and create new content. ML can predict the weather, estimate travel times, recommend songs, auto-complete sentences, summarize articles, and generate never-seen-before images. In basic terms, ML is the process of training a piece of software, called a model, to make useful predictions or generate content (like text, images, audio, or video) from data.

• Machine learning, explained | MIT Sloan

  ## What is machine learning? Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world. [...] Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.” The definition holds true, according to a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or “software 1.0,” to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow. [...] Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images. When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

• What is Machine Learning? | IBM

  Think 2026 Scale advantage with AI and hybrid cloud | Think keynotes # What is machine learning? By Dave Bergmann ## What is machine learning? Machine learning is the subset of artificial intelligence (AI) focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions or predictions without explicit, hard-coded instructions. Machine learning has come to dominate the field of AI: it provides the backbone of most modern AI systems, from forecasting models to autonomous vehicles to large language models (LLMs) and other generative AI tools. [...] The central premise of machine learning (ML) is that if you optimize a model’s performance on a dataset of tasks that adequately resemble the real-world problems it will be used for—through a process called model training—the model can make accurate predictions on the new data it sees in its ultimate use case. Training itself is simply a means to an end: generalization, the translation of strong performance on training data to useful results in real-world scenarios, is the fundamental goal of machine learning. In essence, a trained model is applying patterns it learned from training data to infer the correct output for a real-world task: the deployment of an AI model is therefore called AI inference. [...] The discipline of machine learning is closely intertwined with that of data science. In a sense, machine learning can be understood as a collection of algorithms and techniques to automate data analysis and (more importantly) apply learnings from that analysis to the autonomous execution of relevant tasks. The origin of the term (albeit not the core concept itself) is often attributed to Arthur L. Samuel’s 1959 article in IBM Journal, “Some Studies in Machine Learning Using the Game of Checkers.” In the paper’s introduction, Samuel neatly articulates machine learning’s ideal outcome: “a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.”1 ### Machine learning vs. artificial intelligence

• 10 Machine Learning Applications | Coursera

  ### ## What is machine learning? Machine learning is a subfield of artificial intelligence (AI) that uses models created from algorithms trained on data sets to perform relatively complex tasks that traditionally could only be performed by humans, such as making predictions or categorizing information. As a result, machine learning is one of the most ubiquitous forms of AI used today and accounts for many of the recent advances in the goods and services that people use every day. [...] Machine learning is a subset of AI that is used to power many of the modern world's conveniences and technology, including recommendation engines, fraud detection, and translation software. Machine learning is used across industries, such as finance, tech, media, and medicine. As AI technology becomes more ingrained in our daily lives, learning how it works and is used can help you better understand how to leverage it. In this article, you’ll learn more about machine learning and how it is used. Afterward, if you want to learn more about machine learning, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization. ### ## What is machine learning? [...] # 10 Machine Learning Applications Machine learning is one of the most common forms of artificial intelligence. Discover some of the ways it’s being used today. ![[Featured Image] A group of machine learning engineers stand around a computer, analyzing a machine learning application in the technology field.]( Most of us interact with machine learning almost daily. From personalized recommendations on streaming platforms to financial systems that automatically flag fraudulent transactions, there are countless ways we use AI in our everyday lives. At a glance, here's what you need to know about machine learning applications in the real world:

• Instance Of

  Q5253501

• Inception Date

  1/1/1986