Machine Learning: Difference between revisions

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* [[MATLAB Octave|MATLAB/Octave]]
* [[MATLAB Octave|MATLAB/Octave]]
=Machine Learning=
The science of getting computers to learn without being explicitly programmed ([https://en.wikipedia.org/wiki/Arthur_Samuel Arthur Samuel]).
A computer program is said to ''learn'' from ''experience'' E with respect to some ''task'' T and some ''performance measure'' P, if its performance on T, as measured by P, improves with experience E (Tom Mitchell)
=Neural Networks=
Learning algorithms that mimic how the brain works.
=Natural Language Processing (NLP)=
=Learning Algorithms=
==Supervised Learning==
In supervised learning, we have at our disposal a dataset that tell us which is the "correct answer".
Input variables, output (target) variables.
A pair of one input variable and one output variable is called a training example.
The dataset is called a training set.
Definition: given a training set, learn a function h: X -> Y so that h(x) is a "good" predictor for the corresponding value of y. For historical reason, this function is called hypothesis. The learning algorithm outputs a function (h - hypothesis). The hypothesis function maps input variables to output variables. When the target variable we're trying to predict is continuous, the learning problem is a [[#Regression_Problem|regression problem]]. When the target variable can only take a number of discrete values, we call the problem a [[#Classification_Problem|classification problem]].
The accuracy of the hypothesis is measured by a cost function. A common cost function is the ''squared error function'' or ''mean squared error''.
:[[Image:SquaredErrorCostFunction.png]]
We are trying to minimize the cost function.
Gradient descent is an algorithm to minimize the cost function (and other functions). Local minimum (local optimum). Alpha is the ''learning rate''.
:[[Image:GradientDescent.png]]
"Batch" gradient descent - the algorithm uses the entire training set.
Gradient descent is iterative.
===Regression Problem===
The goal is to predict a continuous value.
See: {{Internal|Regression|Regression}}
===Classification Problem===
A ''classification problem'' is the problem of identifying which category (out of a set of categories) an example belongs to. The goal of a classification problem is to predict a discrete value out of a set of possible discrete values.
See: {{Internal|Classification|Classification}}
==Unsupervised Learning==
* Clustering problems.
==Reinforcement Learning==
==Recommender System==
=Feature=
Synonymous with ''attribute''.
Also ''input''
Infinite number of features.
Feature vector.
=Parameters=
Theta

Revision as of 23:01, 3 January 2018

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