{"id":null,"code":"IE00AK15","name":{"valueFi":"Tilastollinen signaalinkäsittely 1","valueEn":"Statistical Signal Processing 1","valueSv":""},"credits":7.0,"minCredits":7,"maxCredits":7,"tags":[],"createdAt":1790534830148,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Upon completion the student\r\n* Knows basics of probability and statistics and can apply them in signal processing applications in simple analytical calculations and simulations with software simulators.\r\n* Knows the basic concepts of linear filtering in time and frequency domains.\r\n* Knows the key tools of linear algebra and can apply them in solving signal processing problems.\r\n* Understands the key concepts in statistical-based machine learning (such as supervised and unsupervised learning)\r\n* Understand Bayesian and frequentist (classical) approaches to both estimation and detection\r\n* Masters the most important estimation principles such as maximum likelihood, least squares, and posterior mode (MAP) and mean\r\n* Can derive an estimator for a given criterion and basic data models.\r\n* Can use linear regression also with regularization to avoid overfitting.\r\n* Knows and can derive Cramér-Rao lower bound for classical estimation.\r\n* Understands the basics of detection and classification theory (both Bayesian and classical)\r\n* Can use software simulators to simulate and assess the performance of estimators and detectors.\r\n\r\nGeneric skills: “is able to apply analytical and critical thinking skills in a manner appropriate to their discipline, considering the interfaces between fields and new knowledge”, “is able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures“, “is able to apply the methods of their own discipline and recognise the strengths and limitations of methods in other disciplines“","valueEn":"Upon completion the student\r\n* Knows basics of probability and statistics and can apply them in signal processing applications in simple analytical calculations and simulations with software simulators.\r\n* Knows the basic concepts of linear filtering in time and frequency domains.\r\n* Knows the key tools of linear algebra and can apply them in solving signal processing problems.\r\n* Understands the key concepts in statistical-based machine learning (such as supervised and unsupervised learning)\r\n* Understand Bayesian and frequentist (classical) approaches to both estimation and detection\r\n* Masters the most important estimation principles such as maximum likelihood, least squares, and posterior mode (MAP) and mean\r\n* Can derive an estimator for a given criterion and basic data models.\r\n* Can use linear regression also with regularization to avoid overfitting.\r\n* Knows and can derive Cramér-Rao lower bound for classical estimation.\r\n* Understands the basics of detection and classification theory (both Bayesian and classical)\r\n* Can use software simulators to simulate and assess the performance of estimators and detectors.\r\n\r\nGeneric skills: “is able to apply analytical and critical thinking skills in a manner appropriate to their discipline, considering the interfaces between fields and new knowledge”, “is able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures“, “is able to apply the methods of their own discipline and recognise the strengths and limitations of methods in other disciplines“","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"Review of probability and statistics, random variables; linear algebra, linear filtering of random signals, use of software simulators, supervised and unsupervised learning, estimation theory, Cramér-Rao lower bound, linear models, maximum likelihood estimation, least squares estimation, Bayesian estimation, statistical decision theory, receiver operating characteristics, hypothesis testing","valueEn":"Review of probability and statistics, random variables; linear algebra, linear filtering of random signals, use of software simulators, supervised and unsupervised learning, estimation theory, Cramér-Rao lower bound, linear models, maximum likelihood estimation, least squares estimation, Bayesian estimation, statistical decision theory, receiver operating characteristics, hypothesis testing","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 0.7 and that of project report 0.3.\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 0.7 and that of project report 0.3.\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 (lectures) and online teaching (MATLAB sessions) 50h, Matlab simulation exercises in groups 35 h, independent work 100 h.","valueEn":"Face-to-face teaching (lectures) and online teaching (MATLAB sessions) 50h, Matlab simulation exercises in groups 35 h, independent work 100 h.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"<p>Lecture notes, related&nbsp;videos, calculus exercises, Software simulation&nbsp;project guidelines and examples. Available in Moodle.</p>","valueEn":"<p>Lecture notes, related&nbsp;videos, calculus exercises, Software simulation&nbsp;project guidelines and examples. Available in Moodle.</p>","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"The course is held in the autumn semester, during periods 1 and 2. It is recommended to complete the course at the 1st semester of the master studies.\r\n\r\nTarget audience:\r\nElectrical, communications and computer science and engineering students.\r\n\r\nExtra information:\r\nCourse materials etc. can be found in Moodle.","valueEn":"The course is held in the autumn semester, during periods 1 and 2. It is recommended to complete the course at the 1st semester of the master studies.\r\n\r\nTarget audience:\r\nElectrical, communications and computer science and engineering students.\r\n\r\nExtra information:\r\nCourse materials etc. can be found in Moodle.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"1. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Estimation Theory, vol. 1.\" Prentice Hall, 1993. 0-13-345711-7\n2. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2.\" Prentice Hall, 1998. 0-13-504135-X\n3. [main book] Kevin P. Murphy, \"Probabilistic Machine Learning: An introduction\", MIT Press, 2022, [Online, available] https://probml.github.io/pml-book/book1.html 978-0262046824\n4. [signal aspects] Paolo Prandoni and Martin Vetterli, \"Signal Processing for Communications\", 2008, [Online, available] https://www.sp4comm.org/docs/sp4comm_corrected.pdf 978-1420070460\n5. [supplementary book] Kay, Steven M. “Fundamentals of Statistical Signal Processing, Volume 3.” Prentice Hall, 2017. 9780134878409","valueEn":"1. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Estimation Theory, vol. 1.\" Prentice Hall, 1993. 0-13-345711-7\n2. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2.\" Prentice Hall, 1998. 0-13-504135-X\n3. [main book] Kevin P. Murphy, \"Probabilistic Machine Learning: An introduction\", MIT Press, 2022, [Online, available] https://probml.github.io/pml-book/book1.html 978-0262046824\n4. [signal aspects] Paolo Prandoni and Martin Vetterli, \"Signal Processing for Communications\", 2008, [Online, available] https://www.sp4comm.org/docs/sp4comm_corrected.pdf 978-1420070460\n5. [supplementary book] Kay, Steven M. “Fundamentals of Statistical Signal Processing, Volume 3.” Prentice Hall, 2017. 9780134878409","valueSv":"1. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Estimation Theory, vol. 1.\" Prentice Hall, 1993. 0-13-345711-7\n2. [supplementary book] Kay, Steven M. \"Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2.\" Prentice Hall, 1998. 0-13-504135-X\n3. [main book] Kevin P. Murphy, \"Probabilistic Machine Learning: An introduction\", MIT Press, 2022, [Online, available] https://probml.github.io/pml-book/book1.html 978-0262046824\n4. [signal aspects] Paolo Prandoni and Martin Vetterli, \"Signal Processing for Communications\", 2008, [Online, available] https://www.sp4comm.org/docs/sp4comm_corrected.pdf 978-1420070460\n5. [supplementary book] Kay, Steven M. “Fundamentals of Statistical Signal Processing, Volume 3.” Prentice Hall, 2017. 9780134878409"}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"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, 521330A. The recommended prerequisite is the completion of Telecommunication Engineering.\r\n\r\nRecommended optional programme components\r\n521323S Wireless communications I and 031051S Numerical Matrix Analysis are 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, 521330A. The recommended prerequisite is the completion of Telecommunication Engineering.\r\n\r\nRecommended optional programme components\r\n521323S Wireless communications I and 031051S Numerical Matrix Analysis are 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, Lily Lehtomäki","valueEn":"Markku Juntti, Lily Lehtomäki","valueSv":"Markku Juntti, Lily Lehtomäki"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}