Consider m data points (x j, y j), where the y j s are generated from the x j s according to the linear Gaussian model in Equation (20.5). Find the values of θ1, θ2 and σ that maximize the conditional log likelihood of the data.
Answer to relevant QuestionsConsider the noisy-OR model for fever described in Section 14.3. Explain how to apply maximum-likelihood learning to fit the parameters of such a model to a set of complete data.Construct a support vector machine that computes the XOR function. It will be convenient to use values of 1 and —1 instead of I and 0 for the inputs and for the outputs. So an example looks like ([—1. ii, 1) or ([—1, ...Suppose that a training set contains only a single example, repeated 100 times. In 80 of the 100 cases, the single output value is I; in the other 20, it is 0. What will a back- propagation network predict for this example, ...Write out the parameter update equations for TD learning with U (x, y) = θ0 + θ1x + θ2y + θ3 √ (x - xg) 2 + (y - y g) 2.Outline the major differences between Java (or any other computer language with which you are familiar) and English, commenting on the “understanding” problem in each case, think about such things as grammar, syntax, ...
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