Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
Develop understanding and skills for using the tools of probability, signal and systems and learning theory to estimate signals and parameters of interest, to detect events from data, and to learn from data. Develop understanding of multivariate analysis as well. Develop understanding how to identify the optimal estimator/detector or at least bound the performance of any estimator/detector. Develop understanding and skills for using detection approaches (statistical hypotheses testing). Understanding radar detection. Practicing basic concepts such as: sufficient statistics, bias and mean squared error, maximum likelihood, Bayesian estimation and learning.
Schedule: 13.01.2021 - 08.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Visa Koivunen, Esa Ollila, Esa Ollila, Sergiy Vorobyov
Teacher in charge (applies in this implementation): Visa Koivunen, Esa Ollila, Esa Ollila, Sergiy Vorobyov
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Statistical modeling and basic distributions. Parameter estimation. Hypothesis testing detection theory. Basics of learning theory.
Assessment Methods and Criteria
Lectures, exercises, assignments, final exam.
Lectures, exercises, assignments, final exam approximately 50 h, independent work approximately 83 h, total 133 h
Attendance in some contact teaching may be compulsory.
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