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What is Machine Learning? – Week 1 (Part 1)

Machine Learning

  • Grew out of work in AI
  • New Capability for Computers

Examples:

  • Database Mining: Large dataset from growth of automation/web. E.G Web Click Data, Medical Records, Biology, Engineering.
  • Applications can’t program by hand.

Example: Autonomous Helicopter, Handwriting recognition, most of Natural language Processing (NLP), Computer Vision.

  • Self-customizing programs
  • EG Amazon, Netflix product Recommendations
  • Understanding human learning (brain, real AI)

What is Machine Learning?

“Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.”  by Arthur Samuel (1959)

“Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performane measure P, it its performance on T, as measured by P, improves with experience E.” by  Tom Mitchell (1998)

Machine Learning Algorithms:

  • Supervised Learning
  • Unsupervised Learning

Others: Reinforcement Learning, Recommender Systems.

Course Summary by Andrew Ng

What is Machine Learning?

Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.

Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications:

Supervised learning and Unsupervised learning.

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Written by Michael Vicente

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