(31th-Oct-2020)
Choosing a model-class: – A model-class, f, is a way of using some numerical parameters W, to map each input vector, x, into a predicted output y.
• Learning usually means adjusting the parameters to reduce the discrepancy between the target output, t, on each training case and the actual output, y, produced by the model. – For regression, is often a sensible measure of the discrepancy. – For classification there are other measures that are generally more sensible (they also work better).
How supervised learning typically works?
Regarding Unsupervised learning is following.
For about 40 years, unsupervised learning was largely ignored by the machine learning community – Some widely used definitions of machine learning actually excluded it. – Many researchers thought that clustering was the only form of unsupervised learning.
• It is hard to say what the aim of unsupervised learning is. – One major aim is to create an internal representation of the input that is useful for subsequent supervised learning. – You can compute the distance to a surface by using the disparity between two images. But you don’t want to learn to compute disparities by stubbing your toe thousands of times.
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