What is machine learning

Definition of machine learning

• Definition 1
• Arthur Samuel (1959). Machine Learning:field of study, gives computers the ability to learn without being explicitly programmed.
• • The definition given by Arthur Samuel in 1959: Machine learning is a field of study that makes a computer capable of learning in the case of ambiguous programming,
• Definition 2 (not very easy to understand)
• Tom Mitchell (1998) well-posed Learning PROBLEM:A computer program was said to learn from experience E with respect to SOM E Task T and some performance measure p, if its performance on T, as measure by P, improves with experience E.
• • Tom Mitchell in 1998: (Google Translate) Appropriate learning problem: is a computer program from experience E to learn a task T and some performance measurement p, if its performance on T, measured by P, with experience E and improve. (Later on as an example)
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  • Example:suppose your email program watches which emails "do" or "do" mark as spam, and based on this learns how to B etter filter spam. What's the task T in this setting?
    • Classifying emails as spam or not spam. ---T
    • Watching you a label emails as spam or not spam. ---E
    • The number (or fraction) of emails correctly classified as spam/not spam. ---P
  • Example: Suppose your e-mail program observes your flagged spam and non-spam messages and learns how to better filter spam based on observations, what is T in this example?
    • Classify messages as spam and non-spam---T
    • Look at you. Mark e-mail messages as spam or non-spam---E
    • The number of e-mail messages (or fractions) correctly categorized as spam/non-spam---P
  • Example:playing Checkers.

    • E = The experience of playing many games of checkers

    • T = The task of playing checkers.

    • P = The probability that the program would win the next game.
  • Example: Checkers Games
    • E = play a lot of the experience from checkers
    • T = The task of playing checkers
    • P = The possibility of the program winning the game

Classification of machine learning

• Supervised learning (supervised learning)
• Unsupervised learning (unsupervised learning)
• Others:reinforcement Learning, Recommender systems. (Enhanced learning, referral system)

Why is it so popular now?

Machine learning

• Grew out of the work in AI (from the traditional artificial intelligence (can be understood as having its own set of systems))
• New capability for computers (computers can learn by themselves).

Machine Application Field

Examples:

• Database mining (Data mining)
  • Large datasets from growth of automation/web. (Large data sets for automation/network growth)
  • e.g, WEB click Data, medical records, biology, engineering. (Web click Data, medical records, etc.)
• Applications can ' t program by hand (no manual programming Application)
  • E.g, autonomous helicopter, handwriting recognition, most of Natural Language processing (NLP), computer Vision. (Autonomous helicopters, handwriting recognition, natural language processing, computer vision, etc.)
Self-customizing programs (custom program)
• e.g, Amazon, Netfix product recommendations (for example: recommended products by preference or purchase record)
• Understanding human learning (brain, real AI). (Machine learning is how a sensible class learns.)

What is machine learning