(29th-Oct-2020)
It is very hard to write programs that solve problems like recognizing a three-dimensional object from a novel viewpoint in new lighting conditions in a cluttered scene.
It is hard to write a program to compute the probability that a credit card transaction is fraudulent.
Definition Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed[Arthur Samuel,1959]
Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. • A machine learning algorithm then takes these examples and produces a program that does the job. • Massive amounts of computation are now cheaper than paying someone to write a task-specific program.
Some examples of tasks best solved by learning.
Recognizing patterns: – Objects in real scenes – Facial identities or facial expressions – Spoken words
Recognizing anomalies: – Unusual sequences of credit card transactions – Unusual patterns of sensor readings in a nuclear power plant
Prediction: – Future stock prices or currency exchange rates – Which movies will a person like?
There are several types of learning tasks
Supervised learning – Learn to predict an output when given an input vector. – Each training example consists of an input vector x and a target outputt.
Unsupervised learning – Discover a good internal representation of the input
Others: – Reinforcement learning, recommender systems
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