Lehrinhalte
The module provides an introduction to the emerging field of machine learning from an engineering perspective. Important models and learning methods are presented and exemplified through problems from information and communication technology.
[list]
[*]Fundamentals of probability theory and multivariate statistics
[*]Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discriminative)
[*]Probabilistic graphical models: categories, inference and parameter estimation, structure learning, probabilistic programming
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Inference as optimization, variational inference
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literatur
[list]
[*]Kevin P. Murphy. Machine Learning A probabilistic perspective, MIT Press, 2012
[*]Christopher M. Bishop. Pattern recognition and Machine Learning, Springer, 2006
[*]Peter Bühlmann und Sara van de Geer. Statistics of high-dimensional data Methods, theory and applications, Springer, 2011
[/list]
Voraussetzungen
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
Online-Angebote
Moodle
The module provides an introduction to the emerging field of machine learning from an engineering perspective. Important models and learning methods are presented and exemplified through problems from information and communication technology.
[list]
[*]Fundamentals of probability theory and multivariate statistics
[*]Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discriminative)
[*]Probabilistic graphical models: categories, inference and parameter estimation, structure learning, probabilistic programming
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Inference as optimization, variational inference
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literatur
[list]
[*]Kevin P. Murphy. Machine Learning A probabilistic perspective, MIT Press, 2012
[*]Christopher M. Bishop. Pattern recognition and Machine Learning, Springer, 2006
[*]Peter Bühlmann und Sara van de Geer. Statistics of high-dimensional data Methods, theory and applications, Springer, 2011
[/list]
Voraussetzungen
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
Online-Angebote
Moodle
- Lehrende: KleinAnja
- Lehrende: KöpplHeinz
Semester: ST 2020