Lennart Ljung (Linköping University)
Will Machine Learning Change the System Identification Paradigm?
State-of-the-Art System Identification works with well defined model structures and Maximum-likelihood type parameter estimation algorithms. This paradigm is well founded and supported by theory, algorithms, software and industrial applications. Machine Learning tackles essentially the same family of problems, and has been very successful in attracting wide interest, with a (seemingly) different box of tools. The question is what impact this will have on the system identification community. This presentation looks at a few aspects of this question, primarily at the roles of regularization, kernel methods, and Gaussian process regression.