Course Contents
Systems of linear equations and linear least squares problems, linear regression, eigenvalue and singular value decomposition, mean component analysis, Bayes stastistics, ridge regression, dimension reduction, low rank approximation, nonlinear least squares and minimization problems, Newton method, nonlinear regression, LASSO, regularization, interpolation and numerical integration, function approximation, radial basis functions, Monte-Carlo methods, networks for regression, convolutional neural networks, training of networks, deep learning
Literature
Ethem Alpaydin: Maschinelles Lernen, de Gruyter Studium, 2019;
Gilbert Srang: Linear Algebra and Learning from Data, Wellesley Cambridge Press, 2019;
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer , 2008
Preconditions
Mathematics I-III recommended
Systems of linear equations and linear least squares problems, linear regression, eigenvalue and singular value decomposition, mean component analysis, Bayes stastistics, ridge regression, dimension reduction, low rank approximation, nonlinear least squares and minimization problems, Newton method, nonlinear regression, LASSO, regularization, interpolation and numerical integration, function approximation, radial basis functions, Monte-Carlo methods, networks for regression, convolutional neural networks, training of networks, deep learning
Literature
Ethem Alpaydin: Maschinelles Lernen, de Gruyter Studium, 2019;
Gilbert Srang: Linear Algebra and Learning from Data, Wellesley Cambridge Press, 2019;
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer , 2008
Preconditions
Mathematics I-III recommended
- Lehrende: Aidan Chaumet
- Lehrende: Selina Drews
- Lehrende: Kersten Schmidt
- Lehrende: Vsevolod Shashkov
Semester: ST 2023