{"id":31961,"date":"2021-05-19T17:09:40","date_gmt":"2021-05-19T17:09:40","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ei5014-machine-learning-syllabus-for-eie-7th-sem-2019-regulation-anna-university-professional-elective-v\/"},"modified":"2021-05-19T17:09:40","modified_gmt":"2021-05-19T17:09:40","slug":"ei5014-machine-learning-syllabus-for-eie-7th-sem-2019-regulation-anna-university-professional-elective-v","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ei5014-machine-learning-syllabus-for-eie-7th-sem-2019-regulation-anna-university-professional-elective-v\/","title":{"rendered":"EI5014: Machine Learning Syllabus for EIE 7th Sem 2019 Regulation Anna University (Professional Elective -V)"},"content":{"rendered":"

Machine Learning detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2019 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the EIE 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 Electronics & Instrumentation Engineering 7th Sem scheme and its subjects, do visit EIE 7th Sem 2019 regulation scheme<\/a>. For Professional Elective -V scheme and its subjects refer to EIE Professional Elective -V syllabus scheme<\/a>. The detailed syllabus of machine learning is as follows. <\/p>\n

Machine Learning<\/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>Introduction To Machine Learning<\/strong><br \/>\n Objectives of machine learning – Human learning\/ Machine learning – Types of Machine learning:-Supervised Learning – Unsupervised learning – Regression – Classification – The Machine Learning Process:- Data Collection and Preparation – Feature Selection – Algorithm Choice -Parameter and Model Selection – Training – Evaluation – Bias-Variance Tradeoff – Underfitting and Over fitting Problems.\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>Supervised Learning<\/strong><br \/>\n Linearly separable and nonlinearly separable populations – K-Nearest Neighbor – Logistic Regression – Radial Basis Function Network – Support Vector Machines: – Kernels – Risk and Loss Functions – Support Vector Machine Algorithm – Multi Class Classification – Support Vector Regression.\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>Neural Networks<\/strong><br \/>\n Multi Layer Perceptron – Backpropagation Learning Algorithm – Neural Network fundamentals -Activation functions – Types of Loss Function – Optimization: Gradient Descent Algorithm -Stochastic Gradient Descent – Batch Normalization and Dropouts – Applications of Neural Network.\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>Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer Texts in Statistics,2013.<\/li>\n<li>Thomas A. Runkler, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer Vieweg, 2nd Edition,2016.<\/li>\n<\/ol>\n<h4>Reference Books:<\/h4>\n<p align=\"justify\">\n<ol>\n<li>Jiawei Han, MichelineKamber, Jian Pei, Data Mining: Concepts and Techniques: Concepts and Techniques, Elsevier, 2011.<\/li>\n<li>Stephen Marsland, Machine Learning: An Algorithmic Perspective<\/li>\n<li>Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 2011<\/li>\n<li>J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, John Wiley and Sons, 2005.<\/li>\n<\/ol>\n<p align=\"justify\">For detailed syllabus of all the other subjects of Electronics & Instrumentation Engineering 7th Sem, visit <a class=\"rank-math-link\" href=\"..\/category\/eie+7th-sem\">EIE 7th Sem subject syllabuses for 2019 regulation<\/a>. <\/p>\n<p align=\"justify\">For all Electronics & Instrumentation Engineering results, visit <a class=\"rank-math-link\" href=\"https:\/\/www.inspirenignite.com\/anna-university\/anna-university-results\/\">Anna University EIE all semester results<\/a> direct link. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine Learning detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the EIE students. For […]<\/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":[75],"tags":[],"class_list":["post-31961","post","type-post","status-publish","format-standard","hentry","category-eie"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/31961","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=31961"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/31961\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=31961"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=31961"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=31961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}