Course Contents
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)
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Probabilistic graphical models: categories, inference and parameter estimation
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Fundamentals of convex optimization: Solution methods and application in communications
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]High-dimensional statistics (“large p small n” setting), learning dependency structure in high-dimensional data, learning causality relations from observational data.
[*]Sparse estimation, random projections, compressive sensing: Theory and applications in signal processing
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literature
[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]
Preconditions
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
Online Offerings
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)
[*]Regression and classification: theory, methods and ICT applications
[*]Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing
[*]Probabilistic graphical models: categories, inference and parameter estimation
[*]Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics
[*]Fundamentals of convex optimization: Solution methods and application in communications
[*]Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)
[*]Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)
[*]High-dimensional statistics (“large p small n” setting), learning dependency structure in high-dimensional data, learning causality relations from observational data.
[*]Sparse estimation, random projections, compressive sensing: Theory and applications in signal processing
[*]Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications
[/list]
Literature
[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]
Preconditions
Good command of Matlab (for instance knowledge from course 18-st-2030 Matlab Grundkurs) and engineering mathematics
Online Offerings
Moodle
- Lehrende: Anja Klein
- Lehrende: Heinz Köppl
Semester: Verão 2024