{"id":17157,"date":"2025-04-02T15:53:29","date_gmt":"2025-04-02T10:23:29","guid":{"rendered":"https:\/\/civileng.helashop.lk\/?page_id=17157"},"modified":"2025-04-02T15:54:57","modified_gmt":"2025-04-02T10:24:57","slug":"ce5770","status":"publish","type":"page","link":"https:\/\/eng.pdn.ac.lk\/civileng\/ce5770\/","title":{"rendered":"CE5770"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"17157\" class=\"elementor elementor-17157\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f3f40a4 e-con-full e-flex e-con e-parent\" data-id=\"f3f40a4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-744a617 elementor-widget elementor-widget-html\" data-id=\"744a617\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    table {\n        border: 1px solid black;\n        font-family: Verdana;\n        font-size: 12pt;\n        border-collapse: collapse;\n        width: 100%;\n    }\n    td, th {\n        border: 1px solid black;\n        padding: 5px;\n        text-align: left;\n    }\n    @media screen and (max-width: 600px) {\n        table {\n            display: block;\n            overflow-x: auto;\n            white-space: nowrap;\n        }\n    }\n<\/style>\n<table style=\"border-color: black; font-family: Verdana; font-size: 12pt; text-indent: 0; border-collapse:collapse; margin-left:5.25pt;\" border=\"solid\" cellspacing=\"6\" cellpadding=\"5\">\n\n<tbody style=\"vertical-align: top; overflow: visible;\">\n\n<tr>\n<td width=\"301\">\n<p><strong>Semester:<\/strong><\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>7<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"301\">\n<p><strong>Course Code<\/strong>:<\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>CE5770<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"301\">\n<p><strong>Course Name<\/strong>:<\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>Artificial Intelligence and Machine Learning<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"301\">\n<p><strong>Credit Value:<\/strong><\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>2 (Notional hours:100)<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"301\">\n<p><strong>Prerequisites:<\/strong><\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>None<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"301\">\n    <p><strong>Core\/Optional<\/strong><\/p>\n<\/td>\n<td colspan=\"6\" width=\"562\">\n<p>Optional<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td rowspan=\"2\" width=\"301\">\n<p><strong>Hourly Breakdown<\/strong><\/p>\n<\/td>\n<td width=\"129\">\n<p>Lecture hrs.<\/p>\n<\/td>\n<td width=\"121\">\n<p>Tutorial hrs.<\/p>\n<\/td>\n<td colspan=\"2\" width=\"142\">\n<p>Practical hrs.<\/p>\n<\/td>\n<td colspan=\"2\" width=\"170\">\n<p>Independent Learning &amp;\nAssessment hrs.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"129\">\n<p>20<\/p>\n<\/td>\n<td width=\"121\">\n<p>-<\/p>\n<\/td>\n<td colspan=\"2\" width=\"142\">\n<p>20<\/p>\n<\/td>\n<td colspan=\"2\" width=\"170\">\n<p>60<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"7\" width=\"863\">\n<p><strong>Course Aim: <\/strong>To introduce the fundamentals, models and techniques commonly used in artificial intelligence applications, particularly in machine learning.<\/p>\n\n<p><strong>Intended Learning Outcomes<\/strong>:<\/p>\n<p>On successful completion of the course, the students should be able to;<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>explain <\/strong>the advantages of and limitations in choosing Machine Learning methods for different applications.<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>design <\/strong>experiments using Machine Learning methods with the emphasis on evaluation.<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>implement <\/strong>algorithms for selected methods.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"7\" width=\"863\">\n<p><strong>Course Content:<\/strong><\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Introduction to Machine Learning: <\/strong>Applications of machine learning<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Machine Learning Algorithms: <\/strong>Supervised Learning (Classification, Regression and Ranking), Unsupervised Learning (Clustering and Dimensionality Reduction)<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Regression with one input variables: <\/strong>Cost function, Gradient descent algorithm: Minimizing cost function<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Regression with multiple input variables: <\/strong>Methods for improving training and performance of models, such as vectorization, feature scaling, feature engineering and polynomial regression<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Logistic Regression: <\/strong>Classification algorithm: Hypothesis representation, Decision boundary<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Overfitting : <\/strong>Regularization<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Clustering: <\/strong>Introduction to Clustering, The k-means Algorithm, Mean Shift Algorithm<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; <strong>Neural Networks: <\/strong>Nonlinear hypothesis, Model representation, cost function, forward and backward propagation algorithms, Case studies related to Civil Engineering<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"7\" width=\"863\">\n<p><strong>Teaching \/Learning Methods:<\/strong><\/p>\n<p>Classroom lectures, tutorial discussions, software application, group exercises<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"7\" width=\"863\">\n<p><strong>Assessment Strategy:<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr align=\"center\">\n<td colspan=\"2\" width=\"430\">\n<p>Continuous Assessment<\/p>\n\n<p>50%<\/p>\n<\/td>\n<td colspan=\"5\" width=\"433\">\n<p>Final Assessment<\/p>\n\n<p>50%<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"2\" width=\"430\">\n<p>Details: <\/p> \n<p>Assignment\/Quizzes &nbsp;&nbsp;&nbsp;20%<\/p>\n<p>Mini Project &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;30%<\/p>\n<\/td>\n<td colspan=\"2\" width=\"138\">\n<p>Theory (%)<\/p>\n\n<p>50<\/p>\n<\/td>\n<td colspan=\"2\" width=\"176\">\n<p>Practical (%)<\/p>\n\n<p>-<\/p>\n<\/td>\n<td width=\"118\">\n<p>Other (%)<\/p>\n\n<p>-<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"7\" width=\"863\">\n<p><strong>Recommended Reading<\/strong>:<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; Aur&eacute;lien G&eacute;ron, (2019). <em>Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow<\/em>, 2nd edition, O'Reilly Media, Inc.<\/p>\n<p>\u27a2&nbsp;&nbsp;&nbsp; Marc Peter Deisenroth. (2014). <em>Mathematics for Machine Learning<\/em>, 5th edition, Cambridge University Press.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Semester: 7 Course Code: CE5770 Course Name: Artificial Intelligence and Machine Learning Credit Value: 2 (Notional hours:100) Prerequisites: None Core\/Optional Optional Hourly Breakdown Lecture hrs. Tutorial hrs. Practical hrs. Independent Learning &amp; Assessment hrs. 20 &#8211; 20 60 Course Aim: &hellip; <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-17157","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/pages\/17157","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/comments?post=17157"}],"version-history":[{"count":0,"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/pages\/17157\/revisions"}],"wp:attachment":[{"href":"https:\/\/eng.pdn.ac.lk\/civileng\/wp-json\/wp\/v2\/media?parent=17157"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}