{"id":70,"date":"2023-12-18T05:16:57","date_gmt":"2023-12-18T05:16:57","guid":{"rendered":"https:\/\/labs.engineering.asu.edu\/hw\/?page_id=70"},"modified":"2024-10-15T04:44:59","modified_gmt":"2024-10-15T04:44:59","slug":"teaching","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/hw\/teaching\/","title":{"rendered":"Teaching"},"content":{"rendered":"<nav class=\"is-responsive wp-block-navigation is-layout-flex wp-block-navigation-is-layout-flex\" aria-label=\"Navigation\" \n\t\t data-wp-interactive=\"core\/navigation\" data-wp-context='{\"overlayOpenedBy\":{\"click\":false,\"hover\":false,\"focus\":false},\"type\":\"overlay\",\"roleAttribute\":\"\",\"ariaLabel\":\"Menu\"}'><button aria-haspopup=\"dialog\" aria-label=\"Open menu\" class=\"wp-block-navigation__responsive-container-open\" \n\t\t\t\tdata-wp-on--click=\"actions.openMenuOnClick\"\n\t\t\t\tdata-wp-on--keydown=\"actions.handleMenuKeydown\"\n\t\t\t><svg width=\"24\" height=\"24\" 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class=\"wp-block-navigation-item__content\"  href=\"https:\/\/labs.engineering.asu.edu\/hw\/research\/\"><span class=\"wp-block-navigation-item__label\">Research<\/span><\/a><\/li><li class=\"wp-block-navigation-item wp-block-navigation-link\"><a class=\"wp-block-navigation-item__content\"  href=\"https:\/\/labs.engineering.asu.edu\/hw\/teaching\/\"><span class=\"wp-block-navigation-item__label\">Teaching<\/span><\/a><\/li><li class=\"wp-block-navigation-item wp-block-navigation-link\"><a class=\"wp-block-navigation-item__content\"  href=\"https:\/\/labs.engineering.asu.edu\/hw\/students\/\"><span class=\"wp-block-navigation-item__label\">Students<\/span><\/a><\/li><\/ul>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div><\/nav>\n\n<div class=\"uds-hero-sm alignfull has-btn-row \" style=\"margin-bottom:var(--wp--preset--spacing--uds-size-8);\"><div class=\"hero-overlay\"><\/div><img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"498\" src=\"https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled.png\" class=\"hero\" alt=\"\" srcset=\"https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled.png 1920w, https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled-300x78.png 300w, https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled-1024x266.png 1024w, https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled-768x199.png 768w, https:\/\/labs.engineering.asu.edu\/hw\/wp-content\/uploads\/sites\/163\/2023\/12\/Untitled-1536x398.png 1536w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><div class=\"acf-innerblocks-container\">\n\n\n\n<h1 class=\"wp-block-heading has-white-color has-text-color\"><\/h1>\n\n\n\n<div class=\"wp-block-group content is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"has-white-color has-text-color wp-block-paragraph\">Pioneering data-driven algorithms to make actionable actions in the real world.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons btn-row is-layout-flex wp-block-buttons-is-layout-flex\"><\/div>\n\n<\/div><\/div>\n\n<p hidden> <a href=\"https:\/\/clustrmaps.com\/site\/1bxvy\"  title=\"Visit tracker\"><img decoding=\"async\" src=\"\/\/www.clustrmaps.com\/map_v2.png?d=OEriF3wFbrFtJagZkiTpWYopwUKT4dfbW62v-yLg870&#038;cl=ffffff\" \/><\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">CSE 572 &#8211; Data Mining (Fall 2023-Spring2025 @ASU)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Course Number: CSE 572<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Faculty Instructor:&nbsp;<\/strong>Hua Wei, Ph.D.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>E-mail:&nbsp;<\/strong>hua.wei AT asu.edu<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Overview<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This course will introduce fundamental concepts and techniques in data mining including classification, clustering, dimensionality reduction, and outlier detection. Students will learn the theory behind topics as well as gain hands-on experience implementing data mining techniques and applying them to real-world problems and data. Students will learn how data mining is used in research and gain an understanding and practice of the complete research process.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisites<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Students are expected to have a working knowledge of basic probability theory, linear algebra, and the academic research process.<\/li>\n\n\n\n<li><strong>Note: Assignments and projects should be implemented in Python.<\/strong><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Textbook<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">There is no required textbook. Below are some recommended reference books.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Jiawei Han, Micheline Kamber and Jian Pei,&nbsp;<em><u>Data Mining: Concepts and Techniques<\/u><\/em>, 3rd ed.&nbsp;<\/li>\n\n\n\n<li>Chris Bishop,&nbsp;<em><u>Pattern Recognition and Machine Learning<\/u><\/em>.&nbsp;<\/li>\n\n\n\n<li>Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar,&nbsp;<em><u>Introduction to Data Mining<\/u><\/em>, 2nd ed.&nbsp;<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Assignments and Grading<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Participation<\/td><td>5%<\/td><\/tr><tr><td>In class lab assignment<\/td><td>20%<\/td><\/tr><tr><td>Assignment, quiz<\/td><td>35% (5% quiz, 10% assignment 1, 10% assignment 2, 10% assignment 3)<\/td><\/tr><tr><td>Project<\/td><td>40% (5% proposal, 5% literature review, 5% progress report, 15% final presentation, 10% final report)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assignments: All the assignments are done&nbsp;<em>individually<\/em>.&nbsp;<\/li>\n\n\n\n<li>Project: The course project is carried out as a team.&nbsp;<\/li>\n\n\n\n<li>Class attendance: Attending class is required. Excused absences should get approved by the instructor BEFORE the class.&nbsp;<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">IS392 &#8211; Web Mining (Spring 2022\/2023 @NJIT)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Course Number: IS392-002<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Classroom: Tiernan Hall 113 (after Jan. 30)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Class Meets: 11:30 am &#8211; 12:50 pm, Monday &amp; Wednesday,<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Faculty Instructor:&nbsp;<\/strong>Hua Wei, Ph.D.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>E-mail:&nbsp;<\/strong>hua.wei AT njit.edu<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Office:&nbsp;<\/strong>GITC 3803H<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Office Hours:&nbsp;<\/strong>&nbsp;Monday 1-2 pm, or by appointment<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Overview<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This course introduces the design, implementation, and evaluation of web mining applications. Topics include automatic indexing, natural language processing, retrieval algorithms, basic machine learning techniques, and their applications to web data. Students will gain hands-on experience applying theories in case studies.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisites<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>IS218 OR IT114 OR CS114<\/li>\n\n\n\n<li>Programming, linear algebra,&nbsp;probability,&nbsp;algorithm analysis, data structure.<\/li>\n\n\n\n<li><strong>Note: Assignments and projects should be implemented in Python.<\/strong><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Textbook<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">There is no required textbook. Below are some recommended reference books.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Search Engines: Information Retrieval in Practice<\/em>, by Croft, Metzler, and Strohman. Publisher: Addision-Wesley<\/li>\n\n\n\n<li>Paper 1:&nbsp;<a href=\"http:\/\/www.dlib.org\/dlib\/november95\/11croft.html\">What Do People from Information Retrieval?<\/a>, W. Bruce Croft<\/li>\n\n\n\n<li>Paper 2:&nbsp;<a href=\"http:\/\/static.googleusercontent.com\/external_content\/untrusted_dlcp\/www.google.com\/en\/us\/webmasters\/docs\/search-engine-optimization-starter-guide.pdf\"><em>Search Engine Optimization Starter Guide<\/em><\/a>,&nbsp;Google<\/li>\n\n\n\n<li>Jiawei Han, Micheline Kamber and Jian Pei,&nbsp;<em><u>Data Mining: Concepts and Techniques<\/u><\/em>, 3rd ed.&nbsp;<\/li>\n\n\n\n<li>Kevin Patrick Murphy,&nbsp;<em><a href=\"http:\/\/www.cs.ubc.ca\/~murphyk\/MLbook\/\">Machine Learning: a Probabilistic Perspective<\/a><\/em>, 2012.\/li&gt;<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Assignments and Grading<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Assignment, quiz<\/td><td>45% (5% quiz, 14% assignment 1, 13% assignment 2, 13% assignment 3)<\/td><\/tr><tr><td>Project<\/td><td>45% (10% report1, 10% report2, 10% report 3, 15% final report)<\/td><\/tr><tr><td>Class Attendance<\/td><td>10%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assignments: All the assignments are done&nbsp;<em>individually<\/em>.&nbsp;<\/li>\n\n\n\n<li>Project: The course project is carried as a team.&nbsp;<\/li>\n\n\n\n<li>Class attendance: Attending class is required. Excused absence should get approved by the instructor BEFORE the class.&nbsp;<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">IS 657 Spatiotemporal Urban Analytics (Fall 2022 @NJIT)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Course Number: IS657<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Classroom: Jersey City 101<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Class Meets: Tuesday from 6:00 &#8211; 8:50 pm,<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Faculty Instructor:&nbsp;<\/strong>Hua Wei, Ph.D.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>E-mail:&nbsp;<\/strong>hua.wei AT njit.edu<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Office:&nbsp;<\/strong>Faculty office @JerseyCity<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Office Hours:&nbsp;<\/strong>&nbsp;Tuesday 4 &#8211; 6 pm, or by appointment<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Overview<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Cities now generate an immense amount of publicly accessible data that allows us to ask and answer new questions about cities and urban populations. This course will teach the methods, models, and tools for data-driven urban research. You will learn the basics of urban data acquisition, ethics, management, visualization, and statistical analysis with a focus on spatio-temporal data. By the end of the course, you will be able to formulate a question relevant to urban science and then acquire, prepare, and analyze data to gain insights and aid in decision making. The class format will include lectures from the professor, labs using Python, readings, discussion, and student projects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisites<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>IS-665<\/li>\n\n\n\n<li>Basic Python programming knowledge (Required since language instruction will not be covered in class)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Textbook<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">There is no required textbook. Below are some recommended reference books.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Singleton, Spielman, and Folch. \u201cUrban Analytics\u201d<\/li>\n\n\n\n<li>Zheng. \u201cUrban Computing\u201d<\/li>\n\n\n\n<li>Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Assignments and Grading<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Assignment, quiz<\/td><td>40%<\/td><\/tr><tr><td>Project<\/td><td>50%<\/td><\/tr><tr><td>Class Attendance<\/td><td>10%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assignments: All the assignments are done&nbsp;<em>individually<\/em>.&nbsp;<\/li>\n\n\n\n<li>Project: The course project is carried out as a team.&nbsp;<\/li>\n\n\n\n<li>Class attendance: Attending class is required. Excused absences should get approved by the instructor BEFORE the class.&nbsp;<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p class=\"mb-2\">CSE 572 &#8211; Data Mining (Fall 2023-Spring2025 @ASU) Course Number: CSE 572 Faculty Instructor:&nbsp;Hua Wei, Ph.D. E-mail:&nbsp;hua.wei AT asu.edu Overview This course will introduce fundamental concepts and techniques in data mining including classification, clustering, dimensionality reduction, and outlier detection. Students will learn the theory behind topics as well as gain hands-on experience implementing data mining&#8230;<\/p>\n","protected":false},"author":284,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-70","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Teaching - DaRL Group \/ Hua Wei<\/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:\/\/labs.engineering.asu.edu\/hw\/teaching\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Teaching - DaRL Group \/ Hua Wei\" \/>\n<meta property=\"og:description\" content=\"CSE 572 &#8211; Data Mining (Fall 2023-Spring2025 @ASU) Course Number: CSE 572 Faculty Instructor:&nbsp;Hua Wei, Ph.D. E-mail:&nbsp;hua.wei AT asu.edu Overview This course will introduce fundamental concepts and techniques in data mining including classification, clustering, dimensionality reduction, and outlier detection. 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