[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-167237-105":3,"detail-sidebar-cat-1-en-105":80,"doc-detail-167237-en":126},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","honors-in-design-and-dynamics-minor-in-robotics-3rd-to-8th-semester-syllabus-2023-24","Honors in Design and Dynamics - Minor in Robotics - 3rd to 8th Semester Syllabus (2023-24)","","Curriculum and syllabi for the Minor in Robotics under the B.Tech programme in Robotics and Artificial Intelligence (Honors in Design and Dynamics) for the 2023-24 admission batch. Covers four linked modules focusing on elementary numerical methods, applied partial differential equations, basic probability with discrete and continuous distributions, and applied statistics including estimation, confidence intervals, hypothesis testing, regression and correlation. Defines course objectives, measurable outcomes (CO1-CO4), module-wise hours, prerequisites, and aligned reference texts for engineering-level learning.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/paper-templates/","Paper Templates",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/honors-in-design-and-dynamics-minor-in-robotics-3rd-to-8th-semester-syllabus-2023-24/167237/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/honors-in-design-and-dynamics-minor-in-robotics-3rd-to-8th-semester-syllabus-2023-24/167237.png","ImageObject",442,249,{"name":42,"@type":43},"wps_ap_test_251126_0180","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-09-29","2026-08-31",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What are the course prerequisites for this syllabus?","Question",{"text":62,"@type":63},"Students need basic knowledge of calculus, linear algebra, differential equations, and elementary probability. For the robotics part, basic understanding of physics, mathematics (geometry and vectors), and fundamentals of mechanics is required.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which topics are included in Module 1 (Elementary Numerical Methods)?",{"text":67,"@type":63},"The module covers Newton-Raphson and secant methods for solving equations, interpolation using Lagrange/divided difference and Newton forward/backward methods, and numerical integration using Trapezoidal and Simpson’s Rule.",{"name":69,"@type":60,"acceptedAnswer":70},"What learning outcomes are expected by the end of the course?",{"text":71,"@type":63},"Students should demonstrate numerical methods, recognize mathematical models for heat and wave equations and their solution methods, understand probability terminology and distribution functions, and select statistical tools for hypothesis testing and data analysis with interpretation.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},167237,1790268947,{"code":4,"msg":81,"data":82},"success",[83,88,93,98,103,108,113,118,122],{"id":84,"doc_module":22,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},11,"Presentations",90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},15,"Posters",60,"posters",{"id":104,"doc_module":22,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},16,"Social Media",50,"social-media",{"id":109,"doc_module":22,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},17,"Forms",40,"forms",{"id":114,"doc_module":22,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},18,"Letters",30,"letters",{"id":119,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":120,"slug":121},21,5,"papers-templates",{"id":123,"doc_module":22,"doc_module_name":25,"category_name":124,"show_sort_weight":4,"slug":125},158,"General","general-158",{"code":4,"msg":81,"data":127},{"doc_id":78,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":119,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":135,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":141},8796095027276,"https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=","ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH\nTechno Campus, Mahalaxmi Vihar, Ghatikia, Bhubaneswar-751029.\nSchool of Mechanical Sciences\nCURRICULUM AND SYLLABI (2023-24)\nB. Tech. Robotics and Artificial Intelligence\nODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH\nTechno Campus, Mahalaxmi Vihar, Ghatikia, Bhubaneswar-751029.\nSyllabus Structure (Admission Batch: 2023-24)\nSchool of Mechanical Sciences\nProgramme: Robotics and Artificial Intelligence\nAbbreviation used:\n3rd Semester\n4th Semester\n*Minimum 4 weeks of Summer Course / Training / Internship / Skill Course / etc. after 4th Semester\nAbbreviation used:\n5th Semester\n6th Semester\nFOURTH YEAR (SEVENTH SEMESTER)\n*Project / Seminar / Internship\nFOURTH YEAR (EIGHTH SEMESTER)\n*Project / Seminar / Internship\nHonors in Design and Dynamics\nMinor in Robotics\nSchool/ Department: School of Mechanical Sciences\nProgramme: Robotics and Artificial Intelligence\nAbbreviation used:\n3rd Semester\nPrerequisite: Basic knowledge of calculus, linear algebra, differential equations, and elementary probability is required.\nCourse Objectives :\nApply numerical methods for solving equations, interpolation, and numerical integration.\nSolve applied partial differential equations using separation of variables and Fourier series.\nExplain probability concepts and standard probability distributions.\nPerform parameter estimation, hypothesis testing, regression, and correlation analysis.\nCourse Outcome :\nAt the end of the course the student will be able to:\nCO1: Demonstrate the use of common numerical methods such as numerical solutions of equations, interpolation, differentiation and integration etc.\nCO2: Recognize mathematical model of heat equations, wave equations and their solution by appropriate method.\nCO3: Understand the different terminology in probability and have clear concept on probability\ndistribution functions both in continuous and discrete case.\nCO4: Select appropriate statistical tools to investigate a research hypothesis, perform data analysis by applying relevant methodology and interpret result in a variety of settings.\nModule-1: Elementary Numerical Methods \t\t\t\t\t\t(7Hours)\nSolution of algebraic and transcendental equations by Newton-Raphson and secant method.\nInterpolation: Lagrange’s method, divided difference method, Newton’s forward and backward method. Numerical Integration: Trapezoidal and Simpson’s Rule.\nModule- 2: Applied PDE’s \t\t\t\t\t\t\t\t\t(7 Hours)\nElementary PDE’s: separation of variables method to simple problems. One dimensional wave equation:  solution by separation of variables and use of Fourier series,  D' Alembert's solution of wave equation. Normal forms of PDE’s.One dimensional heat equation: solution by Fourier series.\nModule-3: Basic Probability and Probability Distributions\t\t\t\t(7 Hours)\nProbability spaces, conditional probability, independence, Random variables (discrete and continuous), probability mass and density functions, cumulative distribution functions, moments of random variables, mean and variance.\nDiscrete Probability distributions: Binomial, Poisson and hyper-geometric distributions. Continuous Probability distributions: exponential, uniform and normal distributions.\nModule-4: Applied Statistics\t\t\t\t\t\t\t\t(7 Hours)\nRandom sampling, estimation of parameters, maximum likelihood estimation, confidence intervals, testing of hypotheses for mean and variance.\nRegression and correlation analysis: fitting of straight lines (method of lest squares), correlation coefficient with basic properties.\nText Books:\nAdvanced Engineering Mathematics by E. Kreyszig, John Willey & Sons Inc. 10th Edition.\nRonald E. Walpole, Raymond H. Myers, Sharon L. Myers & Keying Ye, “Probability & Statistics for Engineers & Scientists\", Eighth Edition, 2007, Pearson Education Inc., New Delhi.\nReference Books:\nOrdinary and Partial Differential equations by J. Sinha Roy and S. Padhy, Kalyani Publishers.\nHigher Engineering Mathematics by B. V. Ramana, McGraw Hill Education.\nEngineering Mathematics by Pal ","cbCaiq7mbXeUsiFr","https://ap.wps.com/l/cbCaiq7mbXeUsiFr","docx",578840,126,"English","# Honors in Design and Dynamics\n## Minor in Robotics - Syllabus Structure (Admission Batch: 2023-24)\n## Course: Objectives and Outcomes\n## Module 1: Elementary Numerical Methods\n## Module 2: Applied PDE’s\n## Module 3: Basic Probability and Probability Distributions\n## Module 4: Applied Statistics\n## Text Books and Reference Books","[{\"question\":\"What are the course prerequisites for this syllabus?\",\"answer\":\"Students need basic knowledge of calculus, linear algebra, differential equations, and elementary probability. For the robotics part, basic understanding of physics, mathematics (geometry and vectors), and fundamentals of mechanics is required.\"},{\"question\":\"Which topics are included in Module 1 (Elementary Numerical Methods)?\",\"answer\":\"The module covers Newton-Raphson and secant methods for solving equations, interpolation using Lagrange/divided difference and Newton forward/backward methods, and numerical integration using Trapezoidal and Simpson’s Rule.\"},{\"question\":\"What learning outcomes are expected by the end of the course?\",\"answer\":\"Students should demonstrate numerical methods, recognize mathematical models for heat and wave equations and their solution methods, understand probability terminology and distribution functions, and select statistical tools for hypothesis testing and data analysis with interpretation.\"}]","Honors in Design and Dynamics - Minor in Robotics - 3rd to 8th Semester Syllabus (2023-24) | DOCX",1788210597,44]