{"id":170443,"date":"2024-04-06T11:00:05","date_gmt":"2024-04-06T05:30:05","guid":{"rendered":"https:\/\/www.aplustopper.com\/?p=170443"},"modified":"2024-04-06T11:02:56","modified_gmt":"2024-04-06T05:32:56","slug":"ei3752-applied-machine-learning-syllabus","status":"publish","type":"post","link":"https:\/\/www.aplustopper.com\/ei3752-applied-machine-learning-syllabus\/","title":{"rendered":"EI3752 \u2013 Applied Machine Learning Syllabus Regulation 2021 Anna University"},"content":{"rendered":"<p>Applied Machine Learning is the subject of the B.E Instrumentation and Control Engineering Four Year program has a semester VII syllabus. In this article, we would like to discuss the unit-wise syllabus.<\/p>\n<p>We are glad to provide the following syllabus from the student\u2019s point of view. We add the required textbooks and references for students to get all the information regarding the syllabus in one place. It will also assist in gaining knowledge before everyone else as a quick referral. We do not leave a topic regarding the syllabus. Hope this information is useful. Kindly share it with your friends. Comment below for any queries about the Subject code <a href=\"https:\/\/www.aplustopper.com\/ei3752-applied-machine-learning-syllabus\/\">EI3752 \u2013 Applied Machine Learning Syllabus<\/a>.<\/p>\n<p>If you want to know more about the B.E Instrumentation and Control Engineering Syllabus connected to an affiliated institution\u2019s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link\u00a0<a href=\"https:\/\/www.aplustopper.com\/be-instrumentation-and-control-engineering-syllabus-anna-university-regulation-2021\/\" target=\"_blank\" rel=\"noopener\">B.E Instrumentation and Control Engineering Syllabus Regulation 2021 Anna University.<\/a><\/p>\n<p><strong>Aim Of Objectives:<\/strong><\/p>\n<ul>\n<li>To give an introduction on several fundamental concepts and methods for machine learning.<\/li>\n<li>To familiarize with some basic learning algorithms and techniques and their applications.<\/li>\n<li>To provide the knowledge related to processing, analyzing and handling data sets.<\/li>\n<li>To illustrate the typical applications of various clustering-based learning algorithms.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong>EI3752 \u2013 Applied Machine Learning Syllabus<\/strong><\/p>\n<p><strong>Unit &#8211; I:<\/strong> Introduction To Machine Learning<\/p>\n<p>Objectives of machine learning \u2013 Human learning\/ Machine learning \u2013 Types of Machine learning:- Supervised Learning \u2013 Unsupervised learning \u2013 Regression \u2013 Classification \u2013 The Machine Learning Process:- Data Collection and Preparation \u2013 Feature Selection \u2013 Algorithm Choice \u2013 Parameter and Model Selection \u2013 Training \u2013 Evaluation \u2013 Bias-Variance Tradeoff \u2013 Underfitting and Overfitting Problems.<\/p>\n<p><strong>Unit \u2013 II:<\/strong> Data Preprocessing<\/p>\n<p>Data quality \u2013 Data preprocessing: &#8211; Data Cleaning:\u2013 Handling missing data and noisy data \u2013 Data integration:- Redundancy and correlation analysis \u2013 Continuous and Categorical Variables \u2013 Data Reduction:- Dimensionality reduction (Linear Discriminant Analysis \u2013 Principal Components Analysis).<\/p>\n<p><strong>Unit \u2013 III:<\/strong> Supervised Learning<\/p>\n<p>Linearly separable and nonlinearly separable populations \u2013 Logistic Regression \u2013 Radial Basis Function Network \u2013 Support Vector Machines: &#8211; Kernels \u2013 Risk and Loss Functions &#8211; Support Vector Machine Algorithm \u2013 Multi-Class Classification \u2013 Support Vector Regression.<\/p>\n<p><strong>Unit \u2013 IV:<\/strong> Clustering And Unsupervised Learning<\/p>\n<p>Introduction \u2013 Clustering:- Partitioning Methods:- K-means algorithm \u2013 Mean Shift Clustering \u2013 Hierarchical clustering \u2013 Clustering using Gaussian Mixture Models \u2013 Clustering High-Dimensional Data:- Problems \u2013 Challenges.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-170466\" src=\"https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University.png\" alt=\"EI3752 - Applied Machine Learning Syllabus Regulation 2021 Anna University\" width=\"800\" height=\"2000\" srcset=\"https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University.png 800w, https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University-120x300.png 120w, https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University-410x1024.png 410w, https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University-768x1920.png 768w, https:\/\/www.aplustopper.com\/wp-content\/uploads\/2024\/04\/EI3752-Applied-Machine-Learning-Syllabus-Regulation-2021-Anna-University-614x1536.png 614w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/p>\n<p><strong>Unit \u2013 V:<\/strong> Neural Networks<\/p>\n<p>Multi-Layer Perceptron \u2013 Backpropagation Learning Algorithm \u2013 Neural Network fundamentals \u2013 Activation functions \u2013 Types of Loss Function \u2013 Optimization: Gradient Descent Algorithm \u2013 Stochastic Gradient Descent \u2013 one case study.<\/p>\n<p><strong>Text Books:<\/strong><\/p>\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<p><strong>References:<\/strong><\/p>\n<ol>\n<li>EthemAlpaydin, \u2015 Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press 2004.<\/li>\n<li>Stephen Marsland, \u2015 Machine Learning: An Algorithmic Perspective, CRC Press, 2009.<\/li>\n<\/ol>\n<p><strong>Related Posts On Semester &#8211; VII:<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/www.aplustopper.com\/ei3751-industrial-data-communication-syllabus\/\">EI3751 \u2013 Industrial Data Communication<\/a><\/li>\n<li><a href=\"https:\/\/www.aplustopper.com\/ge3791-human-values-and-ethics-syllabus\/\">GE3791 \u2013 Human Values and Ethics<\/a><\/li>\n<\/ul>\n<p><strong>Must Read:<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/www.aplustopper.com\/anna-university-syllabus-regulation-2021\/\">Anna University Syllabus 2021<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Applied Machine Learning is the subject of the B.E Instrumentation and Control Engineering Four Year program has a semester VII syllabus. In this article, we would like to discuss the unit-wise syllabus. We are glad to provide the following syllabus from the student\u2019s point of view. We add the required textbooks and references for students [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":170468,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[115872],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v17.8 (Yoast SEO v17.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>EI3752 \u2013 Applied Machine Learning Syllabus Regulation 2021 Anna University - A Plus Topper<\/title>\n<meta name=\"description\" content=\"Hope this information is useful. 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