Lehrinhalte
classification (support vector machines), clustering, matrix completion, sparse regression, lasso, sparse inverse covariance selection, neural networks (deep learning), Markov random fields
Literature
Mitchell: Machine Learning. Mcgraw-Hill 1997
Murphy: Machine Learning: A Probabilistic Perspective, MIT Press 2012
Sra,Nowozin, Wright: Optimization for Machine Learning, MIT Press, 2012
Miroslav Kubat: An Introduction to Machine Learning.Springer, 2015.
Voraussetzungen
recommended: Introduction to Optimization; useful: Discrete Optimization or Nonlinear Optimization
classification (support vector machines), clustering, matrix completion, sparse regression, lasso, sparse inverse covariance selection, neural networks (deep learning), Markov random fields
Literature
Mitchell: Machine Learning. Mcgraw-Hill 1997
Murphy: Machine Learning: A Probabilistic Perspective, MIT Press 2012
Sra,Nowozin, Wright: Optimization for Machine Learning, MIT Press, 2012
Miroslav Kubat: An Introduction to Machine Learning.Springer, 2015.
Voraussetzungen
recommended: Introduction to Optimization; useful: Discrete Optimization or Nonlinear Optimization
- Lehrende: Stefan Ulbrich
Semester: WT 2022/23