A Simple Problem (Linear Regression) • We have training data X = {x1 k }, k=1,..,N with corresponding output Y = {y k }, k=1,..,N • We want to find the parameters that predict the output Y from the data X in a linear fashion: y k ≈ wo + w1 x1 k x1 y Notations: Superscript: Index of the data point in the training data set; k = k th training data point Subscript: Coordinate of the data point; x1 k = coordinate 1 of data point k.

A Simple Problem (Linear Regression) • It is convenient to define an additional “fake” attribute for the input data: xo = 1 • We want to find the parameters that predict the output Y from the data X in a linear fashion: y k ≈ woxo k + w1 x1

For such proposes, a Backpropagation network is appropiated. If the learning data are free of noise, maybe the adaline can give a good performance

Thank you Prof. Erik Cuevas. Interesting answers. May I ask which methods would be the candidates if there is noise from turbulence?

Thank you.