Michael Vicente
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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