{"id":97,"date":"2018-10-26T14:44:07","date_gmt":"2018-10-26T14:44:07","guid":{"rendered":"http:\/\/groups.cs.umass.edu\/marlin\/?p=97"},"modified":"2018-10-26T14:44:07","modified_gmt":"2018-10-26T14:44:07","slug":"cmpsci-688-probabilistic-graphical-models-2014","status":"publish","type":"post","link":"https:\/\/groups.cs.umass.edu\/marlin\/2018\/10\/26\/cmpsci-688-probabilistic-graphical-models-2014\/","title":{"rendered":"CMPSCI 688: Probabilistic Graphical Models-2014"},"content":{"rendered":"<div class=\"field-type-text field-field-class-semester\">\n<div class=\"odd\">\n<div class=\"inline-first\">Semester: Spring<\/div>\n<\/div>\n<\/div>\n<div class=\"field-type-date field-field-class-year\">\n<div class=\"odd\">\n<div class=\"inline-first\">Offered: 2014<\/div>\n<\/div>\n<\/div>\n<ul>\n<li><strong>Course Description:<\/strong>\u00a0Probabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, graphical models have become invaluable tools in a wide range of areas from computer vision and sensor networks to natural language processing and computational biology. The aim of this course is to develop the knowledge and skills necessary to effectively design, implement and apply these models to solve real problems. The course will cover (a) Bayesian and Markov networks and their dynamic and relational extensions; (b) exact and approximate inference methods; (c) estimation of both the parameters and structure of graphical models. Although the course is listed as a seminar, it will be taught as a regular lecture course with programming assignments and exams. Students entering the class should have good programming skills and knowledge of algorithms. Undergraduate-level knowledge of probability and statistics is recommended. 3 credits.<\/li>\n<\/ul>\n<ul>\n<li><strong>Course Website:<\/strong>\u00a0The course website is hosted on the UMass Moodle portal. Registered students can access it at:\u00a0<a href=\"http:\/\/moodle.umass.edu\/\" rel=\"nofollow\">moodle.umass.edu<\/a>.<\/li>\n<li><strong>Course Text:<\/strong>\u00a0<a href=\"http:\/\/pgm.stanford.edu\/\" rel=\"nofollow\">Probabilistic Graphical Models<\/a>\u00a0by\u00a0<a href=\"http:\/\/ai.stanford.edu\/~koller\/\" rel=\"nofollow\">Koller<\/a>\u00a0and\u00a0<a href=\"http:\/\/www.cs.huji.ac.il\/~nir\/\" rel=\"nofollow\">Friedman<\/a>.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Semester: Spring Offered: 2014 Course Description:\u00a0Probabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, &hellip; <a href=\"https:\/\/groups.cs.umass.edu\/marlin\/2018\/10\/26\/cmpsci-688-probabilistic-graphical-models-2014\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;CMPSCI 688: Probabilistic Graphical Models-2014&#8221;<\/span><\/a><\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-97","post","type-post","status-publish","format-standard","hentry","category-courses","group-blog","no-sidebar","hfeed"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/posts\/97","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/comments?post=97"}],"version-history":[{"count":1,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/posts\/97\/revisions"}],"predecessor-version":[{"id":98,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/posts\/97\/revisions\/98"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/media?parent=97"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/categories?post=97"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/marlin\/wp-json\/wp\/v2\/tags?post=97"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}