stem/AI/Classification/Classification.md
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STEM/AI/Classification/Classification.md
STEM/AI/Classification/Decision Trees.md
STEM/AI/Classification/Gradient Boosting Machine.md
STEM/AI/Classification/Logistic Regression.md
STEM/AI/Classification/Random Forest.md
STEM/AI/Classification/Supervised.md
STEM/AI/Classification/Supervised/README.md
STEM/AI/Classification/Supervised/SVM.md
STEM/AI/Classification/Supervised/Supervised.md
STEM/AI/Learning.md
STEM/AI/Neural Networks/Learning/Boltzmann.md
STEM/AI/Neural Networks/Learning/Competitive Learning.md
STEM/AI/Neural Networks/Learning/Credit-Assignment Problem.md
STEM/AI/Neural Networks/Learning/Hebbian.md
STEM/AI/Neural Networks/Learning/Learning.md
STEM/AI/Neural Networks/Learning/README.md
STEM/AI/Neural Networks/RNN/Autoencoder.md
STEM/AI/Neural Networks/RNN/Deep Image Prior.md
STEM/AI/Neural Networks/RNN/MoCo.md
STEM/AI/Neural Networks/RNN/Representation Learning.md
STEM/AI/Neural Networks/RNN/SimCLR.md
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*Given an observation, determine one class from a set of classes that best explains the observation*
***Features are discrete or continuous***
- 2 category classifier
- Dichotomiser
# Argmax
Argument that gives the maximum value from a target function
# Gaussian Classifier
[Training](Supervised/Supervised.md)
- Each class $i$ has it's own Gaussian $N_i=N(m_i,v_i)$
$$\hat i=\text{argmax}_i\left(p(o_t|N_i)\cdot P(N_i)\right)$$
$$\hat i=\text{argmax}_i\left(p(o_t|N_i)\right)$$
- With equal priors
![](../../img/gaussian-class.png)
# Discrete Classifier
- Each class $i$ has it's own histogram $H_i$
- Describes the probability of each observation type $k$
- $P(o_t=k|H_i)$, based on class-specific type counts
$$\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\right)$$
- Nothing else known about classes
$$\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\cdot P(H_i)\right)$$
- Given class priors $P(H_i)$
- Maximum posterior probability
- Bayes
![](../../img/coordinate-change.png)