{"id":181,"date":"2016-05-10T19:28:57","date_gmt":"2016-05-10T18:28:57","guid":{"rendered":"http:\/\/spagnolini.faculty.polimi.it\/?page_id=181"},"modified":"2025-06-26T15:24:01","modified_gmt":"2025-06-26T14:24:01","slug":"digital-signal-processing-graduate","status":"publish","type":"page","link":"https:\/\/spagnolini.faculty.polimi.it\/?page_id=181","title":{"rendered":"Signal Processing and Learning (graduate)"},"content":{"rendered":"<p><\/p>\n<p style=\"text-align: justify;\">The course focuses on <b>Statistical Signal Processing and Learning <\/b>and covers the following topics:<\/p>\n<ul>\n<li><strong>Review of basics<\/strong>: matrix and linear algebra; quadratic and constrained optimization problems.<\/li>\n<li><strong>Introduction to the estimation problem and models:<\/strong> definitions, performance, sufficient statistics, linear and non-linear models.<\/li>\n<li><strong>Estimators<\/strong>: best linear unbiased estimation (BLUE), maximum likelihood estimation (MLE), least squares method. Cramer Rao lower bound.<\/li>\n<li><strong>Bayesian estimators<\/strong>: a-posteriori estimation (MAP, MMSE and LMMSE); Wiener filter; linear prediction and Yule-Walker equations.<\/li>\n<li><strong>Adaptive filters<\/strong>: LMS, RLS methods, convergence analysis and step-size selection.<\/li>\n<li><strong>Bayesian tracking<\/strong>: dynamic model and Kalman filter; examples of positioning.<\/li>\n<li><strong>2D signals<\/strong> properties and physical filters<\/li>\n<li><strong>Array processing: <\/strong>and direction of arrivals (DOA), beamforming methods.<\/li>\n<li><strong>Pattern and sequence recognition<\/strong>: Bayesian classification of signals in noise, linear discriminant, PCA and clustering methods, supervised classification, deep learning methods and updates.<\/li>\n<li><strong>Montecarlo simulation:<\/strong> and numerical analysis on some use cases.<\/li>\n<\/ul>\n<p><strong>Google<\/strong> is involved in the laboratory activity.<\/p>","protected":false},"excerpt":{"rendered":"<p>The course focuses on Statistical Signal Processing and Learning and covers the following topics: Review of basics: matrix and linear algebra; quadratic and constrained optimization problems. Introduction to the estimation problem and models: definitions, performance, sufficient statistics, linear and non-linear &hellip; <a href=\"https:\/\/spagnolini.faculty.polimi.it\/?page_id=181\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":10,"featured_media":0,"parent":22,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-181","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=181"}],"version-history":[{"count":4,"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/181\/revisions"}],"predecessor-version":[{"id":398,"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/181\/revisions\/398"}],"up":[{"embeddable":true,"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/22"}],"wp:attachment":[{"href":"https:\/\/spagnolini.faculty.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}