Update for 26-02-22 23:00

tech/machine_learning.wiki

@@ -1,3 +1,47 @@
 = Machine Learning =
 
-Machine learning
+Machine learning is a technique in which an algorithm is given data, and over
+time 'learns' to approximate the underlying relationship in said data.
+
+== Types of learning ==
+
+=== Supervised ===
+
+Supervised learningis when an algorithm `F(x)` tages some non-target attribues
+`x` and attempts to approximate some known ground truth, `y`. This is
+supervised, because the expected result of the data is known.
+
+=== Unsupervised ===
+
+Unsupervised learning is when no ground truth for the algorithm is provided. we
+instead focus on finding broad corrleations.
+
+* Clustering is when a given data set is clustered into groups based on
+  similarities among samples in said cluster. For example a cluster made up of
+  customer profiles can be clustered based on possible interests.
+* Association is when an algorithm attempts to find out what clusters appear in
+  a dataset. For example, finding what products tend to be purchased together.
+
+=== Semi-supervised ===
+
+Semi supervised learning is used when only a small portion of the dataset is
+labeled. We can generally either
+
+* train the model on the limited set of labeled datapoints then have it perform
+  unsupervised training on the rest of the data
+* cluster the unlabeled data, then use the sampled data to hone in the
+  clusters
+
+== Output ==
+
+Machine learning models are divided into two types
+
+* Classification (boolean outputs)
+* Regression (continuous outputs)
+
+Classification is used to distinguish between two or more choices.
+
+For regression models the sample is often a Tuple of several factors about the
+sample, and often those fields can be categorical. Each element of a tuple can
+be either categorical (discrete) or numeric (continuous). These elements are
+often called features.