{"id":33990,"date":"2021-05-22T07:40:51","date_gmt":"2021-05-22T07:40:51","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ie5074-machine-learning-algorithms-syllabus-for-manufacturing-8th-sem-2019-regulation-anna-university-professional-elective-vii\/"},"modified":"2021-05-22T07:40:51","modified_gmt":"2021-05-22T07:40:51","slug":"ie5074-machine-learning-algorithms-syllabus-for-manufacturing-8th-sem-2019-regulation-anna-university-professional-elective-vii","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ie5074-machine-learning-algorithms-syllabus-for-manufacturing-8th-sem-2019-regulation-anna-university-professional-elective-vii\/","title":{"rendered":"IE5074: Machine Learning Algorithms Syllabus for Manufacturing 8th Sem 2019 Regulation Anna University (Professional Elective-VII)"},"content":{"rendered":"

Machine Learning Algorithms detailed syllabus for Manufacturing Engineering (Manufacturing) for 2019 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the Manufacturing students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Manufacturing Engineering 8th Sem scheme and its subjects, do visit Manufacturing 8th Sem 2019 regulation scheme<\/a>. For Professional Elective-VII scheme and its subjects refer to Manufacturing Professional Elective-VII syllabus scheme<\/a>. The detailed syllabus of machine learning algorithms is as follows. <\/p>\n

Machine Learning Algorithms<\/title><\/p>\n<h4>Course Objective:<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Unit I<\/h4>\n<p align=\"justify\">\n <strong>Concept Learning<\/strong><br \/>\n A Concept Learning Task: Notation, The Inductive Learning Hypothesis,Concept Learning as Search, FIND-S: Algorithm for finding a Maximally Specific Hypothesis: Version Spaces and the CANDIDATE-ELIMINATION Algorithm; Convergence of CANDIDATE-ELIMINATION Algorithm to the correct Hypothesis; Appropriate Training Examples for learning; Applying Partially Learned Concept, Inductive Bias: A Biased Hypothesis Space; An Unbiased Learner; The Futility of Bias-Free Learning.\n<\/p>\n<h4>Unit II<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Unit III<\/h4>\n<p align=\"justify\">\n <strong>Evaluating Hypotheses<\/strong><br \/>\n Estimating Hypothesis Accuracy: Sample Error and True Error; Confidence Intervals for DiscreteValued Hypotheses. Basics of Sampling Theory: Error Estimation and Estimating Binomial Proportions; the Binomial Distribution; Mean and Variance; Estimators, Bias; and Variance; Confidence Intervals; Two-sided and one-sided bounds. A General approach for deriving confidence intervals: Central Limit Theorem. Difference in Error of two hypotheses; Hypothesis Testing. Comparing Learning Algorithms: Paired t Tests; Practical Considerations.\n<\/p>\n<h4>Unit IV<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Unit V<\/h4>\n<p align=\"justify\">\n <strong>Computational Learning Theory<\/strong><br \/>\n Introduction, probably learning an approximately correct hypothesis: The Problem Setting; Error of a Hypothesis; Learnability. Sample Complexity for Finite Hypothesis Spaces: Agnostic Learning and Inconsistent Hypotheses; Conjunctions of Boolean earnability of Other Concept Classes. Sample Complexity for infinite hypothesis spaces: Shattering a set of Instances; The Vapnik-Chervonenkis Dimension; Sample Complexity and the VC Dimension. The mistake bound model of learning: Mistake bound for the FIND-S Algorithm; Mistake bound for the HALVING Algorithm; Optimal Mistake Bounds; WEIGHTED-MAJORITY Algorithm.\n<\/p>\n<h4>Course Outcome:<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Text Books:<\/h4>\n<p align=\"justify\">\n<ol>\n<li>Machine Learning by Tom M. Mitchell ,McGraw-Hill International Edition, 1997<\/li>\n<\/ol>\n<p align=\"justify\">For detailed syllabus of all the other subjects of Manufacturing Engineering 8th Sem, visit <a class=\"rank-math-link\" href=\"..\/category\/manufacturing+8th-sem\">Manufacturing 8th Sem subject syllabuses for 2019 regulation<\/a>. <\/p>\n<p align=\"justify\">For all Manufacturing Engineering results, visit <a class=\"rank-math-link\" href=\"https:\/\/www.inspirenignite.com\/anna-university\/anna-university-results\/\">Anna University Manufacturing all semester results<\/a> direct link. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine Learning Algorithms detailed syllabus for Manufacturing Engineering (Manufacturing) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the Manufacturing students. For course […]<\/p>\n","protected":false},"author":2297,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[56],"tags":[],"class_list":["post-33990","post","type-post","status-publish","format-standard","hentry","category-manf"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/33990","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/users\/2297"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/comments?post=33990"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/33990\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=33990"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=33990"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=33990"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}