{"id":null,"code":"521324S","name":{"valueFi":"Tilastollinen signaalinkäsittely II","valueEn":"Statistical Signal Processing II","valueSv":""},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[{"code":"opinfi_fitech","title":{"valueFi":"opinfi_fitech","valueEn":"opinfi_fitech","valueSv":"opinfi_fitech"}}],"createdAt":1790534900478,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Upon completion the student will \r\n* understand the key design problems and constraints of the typical inference problems and solutions in statistical signal processing.\r\n* have the skills to apply estimation, detection and learning methods to solve practical problems in basic signal processing applications.\r\n* can use linear algebra, optimization and statistical models to derive algorithms.\r\n* can use numerical analysis to approximate optimal algorithms with iterative solutions including adaptive algorithms.\r\n* understands the basic requirements for the convergence of an iterative, adaptive, and learning algorithm.\r\n* can model the operation of a transceiver using software simulators to assess the performance of inference algorithms.\r\n* can solve simple composite hypothesis testing problems with unknown parameters.","valueEn":"Upon completion the student will \r\n* understand the key design problems and constraints of the typical inference problems and solutions in statistical signal processing.\r\n* have the skills to apply estimation, detection and learning methods to solve practical problems in basic signal processing applications.\r\n* can use linear algebra, optimization and statistical models to derive algorithms.\r\n* can use numerical analysis to approximate optimal algorithms with iterative solutions including adaptive algorithms.\r\n* understands the basic requirements for the convergence of an iterative, adaptive, and learning algorithm.\r\n* can model the operation of a transceiver using software simulators to assess the performance of inference algorithms.\r\n* can solve simple composite hypothesis testing problems with unknown parameters.","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"Bayesian estimators and filters, sequential Bayesian and least squares algorithms, Wiener and Kalman filtering, iterative algorithms, adaptive filtering and learning algorithms, statistical decision theory for signals with unknown parameters, application examples.","valueEn":"Bayesian estimators and filters, sequential Bayesian and least squares algorithms, Wiener and Kalman filtering, iterative algorithms, adaptive filtering and learning algorithms, statistical decision theory for signals with unknown parameters, application examples.","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study Methods","valueSv":""},"content":{"valueFi":"Completing the simulation project tasks, and mid-term exams during the course.\r\nAn individual mid-term exam cannot be retaken. Instead each retake exam arranged after the teaching semester covers the whole course contents. Two retake exams will be arranged.\r\nIn the final grade of the course, the weight for the examination is 0.6 and that of project report 0.4.\r\nRead more about assessment criteria at the University of Oulu webpage.","valueEn":"Completing the simulation project tasks, and mid-term exams during the course.\r\nAn individual mid-term exam cannot be retaken. Instead each retake exam arranged after the teaching semester covers the whole course contents. Two retake exams will be arranged.\r\nIn the final grade of the course, the weight for the examination is 0.6 and that of project report 0.4.\r\nRead more about assessment criteria at the University of Oulu webpage.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Teaching Methods","valueSv":""},"content":{"valueFi":"Face-to-face teaching and e-learning tool usage \r\nFace-to-face teaching and/or online teaching (lectures and exercises) 50h, Matlab simulation exercises in groups 30 h, independent work & passed assignment 50 h.","valueEn":"Face-to-face teaching and e-learning tool usage \r\nFace-to-face teaching and/or online teaching (lectures and exercises) 50h, Matlab simulation exercises in groups 30 h, independent work & passed assignment 50 h.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"

Parts from books:

\r\n\r\n

1. Kevin P. Murphy, “Probabilistic Machine Learning – Introduction,” MIT Press 2022.
\r\n2. Kevin P. Murphy, “Probabilistic Machine Learning – Advanced Topic,” MIT Press 2023.
\r\n3. Steven M Kay, "Fundamentals of statistical signal processing: estimation theory," vol. 1. Prentice Hall 1993.
\r\n4. Steven M. Kay, "Fundamentals of statistical signal processing: Detection theory,” vol. 2. Prentice Hall 1998.
\r\n5. Simon Haykin, “Adaptive Filter Theory”, 3rd ed. or newer, Prentice Hall 1996.
\r\n6. Other literature, lecture notes and material.

","valueEn":"

Parts from books:

\r\n\r\n

1. Kevin P. Murphy, “Probabilistic Machine Learning – Introduction,” MIT Press 2022.
\r\n2. Kevin P. Murphy, “Probabilistic Machine Learning – Advanced Topic,” MIT Press 2023.
\r\n3. Steven M Kay, "Fundamentals of statistical signal processing: estimation theory," vol. 1. Prentice Hall 1993.
\r\n4. Steven M. Kay, "Fundamentals of statistical signal processing: Detection theory,” vol. 2. Prentice Hall 1998.
\r\n5. Simon Haykin, “Adaptive Filter Theory”, 3rd ed. or newer, Prentice Hall 1996.
\r\n6. Other literature, lecture notes and material.

","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Ajoitus \r\nThe course is held in the spring semester, during period 3. It is recommended to complete the course at the 1st spring semester of the master studies. \r\n \r\nKohderyhmä \r\nElectrical, communications and computer science and engineering students. \r\n \r\nLisätiedot \r\nLecture materials etc. can be found on Moodle.","valueEn":"Timing \r\nThe course is held in the spring semester, during period 3. It is recommended to complete the course at the 1st spring semester of the master studies. \r\n \r\nTarget group \r\nElectrical, communications and computer science and engineering students. \r\n \r\nOther information \r\nLecture materials etc. can be found on Moodle.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"Esitietovaatimukset \r\nThe required prerequisite is the completion of the following courses prior to enrolling for the course: 031080A Signal Analysis, 031021P Probability and Mathematical Statistics, 031078P Matrix Algebra, 521348S Statistical Signal Processing I. The recommended prerequisite is the completion of 521330A Telecommunications Engineering, 521323S Wireless Communications I, 031025A Introduction to Optimization and 031051S Numerical Matrix Analysis. \r\n \r\nYhteydet muihin opintojaksoihin \r\n521317S Wireless communications II is recommended to be taken in parallel.","valueEn":"Prerequisites and co-requisites \r\nThe required prerequisite is the completion of the following courses prior to enrolling for the course: 031080A Signal Analysis, 031021P Probability and Mathematical Statistics, 031078P Matrix Algebra, 521348S Statistical Signal Processing I. The recommended prerequisite is the completion of 521330A Telecommunications Engineering, 521323S Wireless Communications I, 031025A Introduction to Optimization and 031051S Numerical Matrix Analysis. \r\n \r\nRecommended optional programme components \r\n521317S Wireless communications II is recommended to be taken in parallel.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"1-5/HYL","valueEn":"1-5/FAIL","valueSv":"1-5/FAIL"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"The course utilizes a numerical grading scale 1-5. In the numerical scale zero (0) stands for a fail.","valueEn":"The course utilizes a numerical grading scale 1-5. In the numerical scale zero (0) stands for a fail.","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Evaluation criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Evaluation criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Evaluation criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Pääasiallinen opetuskieli","valueEn":"Primary Teaching Language","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":"Syventävät opinnot"}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietoliikennetekniikka","valueEn":"Communications Engineering","valueSv":""}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Markku Juntti, Nhan Nguyen","valueEn":"Markku Juntti, Nhan Nguyen","valueSv":"Markku Juntti, Nhan Nguyen"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"opinfi_fitech","valueEn":"opinfi_fitech","valueSv":"opinfi_fitech"}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}