The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning.

The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.

The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course is also an introduction to "abstract" algebra and some of its applications in coding and signal processing.

The course is about some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.

The course is about some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.

The Laboratory courses in the 5th and 6th semesters enable the students to put the the contents of the courses from the four first semesters to the test and to consolidate the aquired knowledge. Furthermore students have the possibilty to gain specific knowledge in certain software packages as MATLAB.

The Laboratory courses in the 5th and 6th semesters enable the students to put the the contents of the courses from the four first semesters to the test and to consolidate the aquired knowledge. Furthermore students have the possibilty to gain specific knowledge in certain software packages as MATLAB.

Mathematical basics of estimation and machine learning, with a view towards applications in signal processing.

The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.

The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.

Mathematical methods in signal processing and machine learning.I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.