{"id":2251,"date":"2020-07-17T15:34:58","date_gmt":"2020-07-17T19:34:58","guid":{"rendered":"https:\/\/ise.ncsu.edu\/fuzzy-neural\/?page_id=2251"},"modified":"2023-07-29T00:18:10","modified_gmt":"2023-07-29T04:18:10","slug":"syllabus","status":"publish","type":"page","link":"https:\/\/ise.ncsu.edu\/fuzzy-neural\/courses\/ise-789-or-791-optimization-models-for-systems-analytics\/syllabus\/","title":{"rendered":"ISE 789\/OR 791 Syllabus"},"content":{"rendered":"\n\n<h2>ISE 789\/OR 791 Syllabus<\/h2>\n<h2>Spring 2022<\/h2>\n<p><!-- \/wp:post-content --><!-- wp:spacer {\"height\":20} --><\/p>\n<div class=\"wp-block-spacer\" style=\"height: 20px\" aria-hidden=\"true\">\u00a0<\/div>\n<p><!-- \/wp:spacer --><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Time<\/h3>\n<p><!-- \/wp:heading --><!-- wp:paragraph --><\/p>\n<p>Tu, Th 11:45 AM \u2013 1:00\u00a0PM<\/p>\n<p>* DELTA Classroom System will automatically record our lectures at the published class starting and ending times and on the published class days. Registered students can view the recorded lectures in our ISE 789\/OR 791 classroom content folder at: <a href=\"https:\/\/ncsu.hosted.panopto.com\/Panopto\/Pages\/Sessions\/List.aspx#folderID=258ed81c-a9d3-45c4-8d7a-ae0000c121df\">&lt;https:\/\/ncsu.hosted.panopto.com\/Panopto\/Pages\/Sessions\/List.aspx#folderID=258ed81c-a9d3-45c4-8d7a-ae0000c121df&gt;<\/a><\/p>\n<p>* DELTA also provides a synchronized video service with a 40-second delay at the same link to enable you attending the class online.<\/p>\n<p>* If a student opens the course folder before the start time of the webcast, then they will need to refresh their browser and select the title of the webcast. The webcast will then open in a new tab. Because of the 40 second delay, the following message will appear: \u201cThis page will update once the webcast begins\u201d. After 40 seconds the webcast will begin.<br \/>* If a student opens an individual webcast link and attempts to view it before the webcast start time then the following message will appear: \u201cThis page will update once the webcast begins\u201d. After 40 seconds the webcast will begin.<\/p>\n<h3>Room<\/h3>\n<p><!-- \/wp:paragraph --><!-- wp:paragraph --><\/p>\n<p>2341 Fitts-Woolard Hall<\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Instructor<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:paragraph --><\/p>\n<p>Dr. S.-C. Fang (<a href=\"mailto:fang@ncsu.edu\">fang@ncsu.edu<\/a>)<\/p>\n<p><!-- \/wp:paragraph --><\/p>\n<p><!-- wp:list --><\/p>\n<ul>\n<li><strong>Office:<\/strong> 4341 Fitts-Woolard Hall<\/li>\n<li><strong>Office Hours:<\/strong>\u00a0<\/li>\n<\/ul>\n<p><!-- \/wp:list --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Teaching Assistant<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:paragraph --><\/p>\n<p>TBA<\/p>\n<p><!-- \/wp:paragraph --><!-- wp:paragraph --><\/p>\n<p><!-- \/wp:paragraph --><!-- wp:list --><\/p>\n<ul>\n<li><strong>Office:\u00a0<\/strong><\/li>\n<li><strong>Office Hours:\u00a0<\/strong><\/li>\n<\/ul>\n<p><!-- \/wp:list --><!-- wp:list --><\/p>\n<p><!-- \/wp:list --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Prerequisite<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ol>\n<li>ISE\/OR\/MA 505: Linear Programming<\/li>\n<li>Programming using CPlex, Gurobi, CVX, SeDuMion MATLAB or equivalent<\/li>\n<\/ol>\n<p style=\"text-align: left\">\u00a0 \u00a0 \u00a0(OR\/MA 706: Nonlinear Programming or ISE\/OR 708: Integer Programming will help.)<\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Course Objectives<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ol>\n<li>This graduate-level course intends to provide the fundamental theory and solution methods of commonly used optimization models for analytic system decision-making with dynamic data.<\/li>\n<li>It involves mathematical modeling, analytic methods, machine learning, and data-driven business decision-making.<\/li>\n<li>It is a flexibly structured experimental course that will evolve into a regular course.<\/li>\n<\/ol>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>Course Content<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ol style=\"list-style-type: upper-roman\">\n<li>Optimization models\n<ol style=\"list-style-type: lower-roman\">\n<li>Linear Optimization<\/li>\n<li>Quadratic Optimization<\/li>\n<li>\u00a0Integer Optimization<\/li>\n<li>Linear Conic Optimization<\/li>\n<li>Nonlinear Optimization<\/li>\n<\/ol>\n<\/li>\n<li>Machine learning approaches\n<ol style=\"list-style-type: lower-roman\">\n<li>Supervised learning for classification and prediction<br \/>Support Vector Machines &amp; Regression (SVM &amp; SVR)<br \/>Artificial Neural Networks (ANN)<\/li>\n<li>\u00a0Unsupervised learning for clustering and featuring<br \/>Similarity Learning and Sparse Solutions<\/li>\n<li>Reinforcement learning in a dynamic environment<br \/>Markov Decision Process and Dynamic Programming<br \/>(ISE 789-II Stochastic Models for Systems Analytics \u2013 Prof. Yunan Liu in Spring 2021)<\/li>\n<\/ol>\n<\/li>\n<li>Systems and data\n<ol style=\"list-style-type: lower-roman\">\n<li>Knowledge systems<br \/>-collaborative filtering (Jester, MovieLens datasets)<br \/>-classification (UCI repository)<\/li>\n<li>Financial investment systems<br \/>-Markowitz mean-variance portfolio selection<br \/>(S&amp;P\u2019s Compustat North America database)<\/li>\n<li>Electric systems<br \/>-data compression and sparse signal recovery<br \/>(Sparco database)<\/li>\n<li>\u00a0Power systems<br \/>-load forecasting (GEFCom2012)<br \/>-unit commitment (MATPOWER)<\/li>\n<li>Aviation systems<br \/>-failure prediction (NASA aircraft engine dataset)<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Homework<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<p>There will be 5 assignments biweekly.<\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Project<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ul>\n<li>End-of-semester report and presentation.<\/li>\n<li>At most 2 persons a team.<\/li>\n<\/ul>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Exam<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<p>A comprehensive take-home exam will be given after finishing Part II.<\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:heading {\"level\":3} --><\/p>\n<h3>Grades<\/h3>\n<p><!-- \/wp:heading --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ul>\n<li>Homework -50%<\/li>\n<li>Course project -30%<\/li>\n<li>Exam -20%<\/li>\n<\/ul>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>Evaluation Standard<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ul>\n<li>A -88 and above<\/li>\n<li>B -75 to 87<\/li>\n<li>C -60 to 74<\/li>\n<li>Fail -under 60<\/li>\n<\/ul>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>Classroom Rules<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<ul>\n<li><strong>Rule 1<\/strong>: No late homework without pre-approval.<\/li>\n<li><strong>Rule 2<\/strong>: Turn in your homework through email.<\/li>\n<li><strong>Rule 3<\/strong>: Office hours can be held through Google Meet.<\/li>\n<li><strong>Rule 4<\/strong>: No make-up exam without pre-approval or an official &#8220;doctor\u2019s note&#8221;.<\/li>\n<\/ul>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>End-of-Semester Class Evaluation<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<p>Online class evaluations will be available for students to complete during the last two weeks of class. Students will receive an email message directing them to a website where they can log in using their Unity ID and complete evaluations. All evaluations are confidential; instructors will never know how anyone student responded to any question, and students will never know the ratings for any particular instructors.<\/p>\n<p>Evaluation Website:\u00a0<a href=\"https:\/\/classeval.ncsu.edu\">https:\/\/classeval.ncsu.edu<\/a><\/p>\n<p>Student help desk:\u00a0classeval@ncsu.edu<\/p>\n<p>More information about ClassEval:\u00a0<a href=\"http:\/\/www2.acs.ncsu.edu\/UPA\/classeval\/index.html\">http:\/\/www2.acs.ncsu.edu\/UPA\/classeval\/index.htm<\/a><\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>Academic Integrity<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<p>A student is expected to know what constitutes academic misconduct found in the <a href=\"https:\/\/policies.ncsu.edu\/policy\/pol-11-35-01\/\">Code of Student Conduct Policy ( POL11.35.1)<\/a>, and to take responsibility for his\/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about \u201crules\u201d for group work\/collaboration should seek guidance from the course instructor.<\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<h3>Students with Disabilities<\/h3>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>\n<p><!-- wp:tadv\/classic-paragraph --><\/p>\n<p>North Carolina State University retains authority, through the Disability Services Office (located in Student Health Services Building, Suite 2221), in determining appropriate accommodations after giving consideration to the preferences of the student, the documentation provided, and institutional expertise in working with students with disabilities. If you require academic accommodations to lessen the impact of your disability, please register with the Disability Services Office at the beginning of each academic term.<\/p>\n<p><!-- \/wp:tadv\/classic-paragraph --><\/p>","protected":false},"excerpt":{"rendered":"<p>ISE 789\/OR 791 Syllabus Spring 2022 \u00a0 Time Tu, Th 11:45 AM \u2013 1:00\u00a0PM * DELTA Classroom System will automatically record our lectures at the&#8230;<\/p>\n","protected":false},"author":149,"featured_media":0,"parent":2238,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-landing.php","meta":{"_acf_changed":false,"ngg_post_thumbnail":0,"footnotes":""},"class_list":["post-2251","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ISE 789\/OR 791 Syllabus - The Fuzzy-Neural Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ise.ncsu.edu\/fuzzy-neural\/courses\/ise-789-or-791-optimization-models-for-systems-analytics\/syllabus\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ISE 789\/OR 791 Syllabus - 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