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Could anyone explain to me the differences & similarities between machine learning and system identifications? Are these just two names of the same thing? In this page, they say:

Machine learning and system identification communities are faced with similar problems where one needs to construct a model from limited or noisy observations.

I've also read the early chapters of the famous book Pattern Recognition and Machine Learning by Christopher M. Bishop. So far, my conclusion is that the problem that system identification is trying to solve is a subset of what machine learning is trying to solve.

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2 Answers 2

System identification is the science of constructing dynamical models from observed data. There are two main approaches: Prediction Error Identification (PEI) and Subspace Identification (SID). Both of them are delivering a so called parametric model, that is to say, a model of a fixed structure. Usually it is the case that the user selects the structure of the underlying system (especially in the PEI methods) or at least the order of the system (in both methods). Even though it is not necessary, a low order system is sought (that is to say, the number of the basis coefficients is relatively small) because it is often used for control purposes, so we have to keep it as simple as possible to avoid computational issues etc. This model can be used to make predictions about the future behaviour of the system given some inputs.

On the other hand, machine learning (ML) has two main branches, classification and regression algorithms. The latter ones are also used for prediction purposes. Two of the most famous approaches in machine learning are Support Vector Machines (SVM) and Gaussian processes (GP). The main difference with the system identification techniques is that the ML techniques are delivering a non-parametric model. The latter means that the prediction for a new input is given as a function of the data points used for the "training" (learning, identification) of the model. Therefore, if we used N=1000 data points for the training, then the prediction would be expressed as a function of these data points. ML methods are more flexible since they don't require any structure selection from the user, but they face other limitations (e.g. computational effort).

Until recently the ML and the system identification techniques were developing independently. But in the latter years there is a great effort to establish a common ground (e.g. see the paper "Four encounters with system identification" from Ljung)

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Machine Learning: modeling for static model and dynamic model , System Identification: focus on dynamic model or dynamic process

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You answer is a bit terse, could you elaborate your answer a bit to provide more detail - for example what is the difference (if any - I'm no expert) between the machine learning dynamic modelling and the system identification dynamic modelling - or are you saying that system identification focuses only on dynamic machine learning, whereas the broad area has a static component? (Just ideas for how you could expand your answer to make it better - maybe they're not good ones) –  Luke Mathieson Mar 30 '13 at 7:26

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