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Machine Learning - Coggle Diagram
Machine Learning
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What ?
“a field of
study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel
coaching computers to intelligently perform tasks beyond traditional number crunching by learning the surrounding environment through repeated examples.
a computational process that uses input data to achieve a desired task without being literally programmed (i.e., “hard coded”) to produce a particular outcome.
How ?
Training : machine learning algorithms are in a sense “soft coded” in that they automatically alter or adapt their architecture through repetition (i.e., experience) so that they become better and better at achieving the desired task.(samples of input data are provided along with desired outcomes).
Learning : The algorithm then optimally configures itself so that it can not only produce the desired outcome when presented with the training inputs, but can generalize to produce the desired outcome from new, previously unseen data
Learning types
Supervise learning
what ?
used to estimate an unknown (input, output) mapping from known (input, output) samples, where the output is labeled (e.g., classifi cation and regression).
has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. Which means some data is already tagged with the correct answer.
How ?
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Linear Regression
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Linear regression types
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Use cases
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Exemples
The objective is to distinguish apples from oranges. Every apple and orange is unique, but we are still able (usually) to tell one from the other. Rather than hard code a machine with many, many exact representations of apples and oranges, it can be programmed to learn to distinguish them through repeated experience with actual apples and oranges.
Unsepervise learning
What ?
only input samples are given to the learning system (e.g., clustering and estimation of probability density function).
training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. "The algorithm finds its own way from the training input data"
Exemples
the objective is to throw a dart at a bull’s-eye. The device (or human) has a variety of degrees of freedom in the mechanism that controls the path of the dart. Rather than try to exactly program the kinematics a priori, the learner practices throwing the dart. For each trial, the kinematic degrees of freedom are adjusted so that the dart gets closer and closer to the bull’s-eye.
How ?
Clustering
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Use cases
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Application
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Why ?
The degrees of the complexity of some processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes