Difference between revisions of "Machine Learning"
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| Line 10: | Line 10: | ||
== Classification Problem == | == Classification Problem == | ||
| + | === Cocktail Party Problem === | ||
| + | * Algorithm | ||
| + | ** [W, s, v ] = svd((repmat(sum(x.*x, 1), size(x, 1), 1).*x)*x'); | ||
| + | |||
$\log yh_\theta(x) + (1-y) \log (1-h_\theta(x))$ comes from Maximum Likelihood Method in Statistics | $\log yh_\theta(x) + (1-y) \log (1-h_\theta(x))$ comes from Maximum Likelihood Method in Statistics | ||
Revision as of 23:38, 19 October 2014
Types of Machine Learning
- Supervised Learning
- Regression Problem: Continuous valued output.
- Classification Problem: Discrete valued output.
- Unsupervised Learning
- Clustering
Linear Regression
Classification Problem
Cocktail Party Problem
- Algorithm
- [W, s, v ] = svd((repmat(sum(x.*x, 1), size(x, 1), 1).*x)*x');
$\log yh_\theta(x) + (1-y) \log (1-h_\theta(x))$ comes from Maximum Likelihood Method in Statistics