Suchergebnis: Katalogdaten im Herbstsemester 2019

DAS in Data Science Information
Kernfächer
Einführungskurse
NummerTitelTypECTSUmfangDozierende
227-0427-00LSignal Analysis, Models, and Machine LearningW6 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
Capstone-Projekt
NummerTitelTypECTSUmfangDozierende
266-0100-00LCapstone Project Belegung eingeschränkt - Details anzeigen
Only for DAS in Data Science.
O8 KP17AF. Perez Cruz, O. Verscheure, Professor/innen
KurzbeschreibungThe capstone project is part of the DAS in Data Science and is an opportunity to apply the knowledge acquired in the program in an independent, real-world project.
Deadline for a project the following semester conducted at the SDSC is mid June/mid December.
LernzielTo apply the knowledge acquired in the program in an independent, real-world project.
InhaltThe capstone project can be done under the supervision of the Swiss Data Science Center, or of any core or adjunct faculty of Data Science.
  •  Seite  1  von  1