Suchergebnis: Katalogdaten im Herbstsemester 2020

DAS in Data Science Information
Kernfächer
Einführungskurse
NummerTitelTypECTSUmfangDozierende
227-0427-00LSignal Analysis, Models, and Machine Learning
Findet dieses Semester nicht statt.
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).
W6 KP4GH.‑A. Loeliger
KurzbeschreibungMathematical 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.
LernzielThe course is an introduction to some basic topics in signal processing and machine learning.
InhaltPart 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 maximization, linear Gaussian models with sparse events.
SkriptLecture notes.
Voraussetzungen / BesonderesPrerequisites:
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory
227-0105-00LIntroduction to Estimation and Machine Learning Belegung eingeschränkt - Details anzeigen W6 KP4GH.‑A. Loeliger
KurzbeschreibungMathematical basics of estimation and machine learning, with a view towards applications in signal processing.
LernzielStudents master the basic mathematical concepts and algorithms of estimation and machine learning.
InhaltReview of probability theory;
basics of statistical estimation;
least squares and linear learning;
Hilbert spaces;
Gaussian random variables;
singular-value decomposition;
kernel methods, neural networks, and more
SkriptLecture notes will be handed out as the course progresses.
Voraussetzungen / Besonderessolid basics in linear algebra and probability theory
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