227-0427-00L Signal and Information Processing: Modeling, Filtering, Learning
|Semester||Autumn Semester 2016|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Abstract||Fundamentals in signal processing, detection/estimation, and machine learning. |
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: kernel methods, neural networks.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, parameter estimation.
|Objective||The course is an introduction to some basic topics in signal processing, detection/estimation theory, and machine learning.|
|Content||Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis.|
Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods.
Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximisation, sparse Bayesian learning.
|Lecture notes||Lecture notes.|
|Prerequisites / Notice||Prerequisites: |
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory