{"version":"1.0","provider_name":"i40","provider_url":"https:\/\/i40.dit.uoi.gr","author_name":"Dimitris Mitrogiorgos","author_url":"https:\/\/i40.dit.uoi.gr\/index.php\/author\/dim_mitrogiorgos\/","title":"Machine Learning for Industry - i40","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"LiW7VNoe2N\"><a href=\"https:\/\/i40.dit.uoi.gr\/index.php\/en\/machine-learning-for-industry\/\">Machine Learning for Industry<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/i40.dit.uoi.gr\/index.php\/en\/machine-learning-for-industry\/embed\/#?secret=LiW7VNoe2N\" width=\"600\" height=\"338\" title=\"&#8220;Machine Learning for Industry&#8221; &#8212; i40\" data-secret=\"LiW7VNoe2N\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/i40.dit.uoi.gr\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Semester A Mandatory Remote Machine Learning for Industry From basic ML techniques to deep learning models and their industrial applications: demand forecasting, anomaly detection, predictive maintenance, and supply chain analysis. ECTS Credits 0 Weeks 0 Total Hours 0 Bibliography Sources 0 Learning Outcomes\u200b 1) Understanding basic machine learning techniques and their theoretical foundations 2) Analysis and Understanding of Machine Learning (ML) Algorithms 3) Implementation of ML techniques using appropriate programming tools (Python, etc.) 4) Use of evaluation metrics (Precision, Recall, F1-score, ROC-AUC) to evaluate models 5) ML application for demand forecasting, anomaly detection, and predictive maintenance 6) Combining ML techniques to solve problems across different scientific fields 7) Understanding the use of ML techniques in industrial practice 8) Conducting a comprehensive literature review on ML techniques General Skills\u200b Adapting to new situations Decision-making Independent project Team project Syllabus Week Topic 1 Introduction to Machine Learning and Types of Learning 2 Regression Methods 3 Data Classification and Adaptation Using k-Nearest Neighbors 4 Artificial Neural Networks and Their Types 5 Radial Basis Function Networks and the k-Means Algorithm 6 Support Vector Machines 7 Decision Trees 8 Feature Selection and Construction Techniques 9 Deep Learning Models 10 Recurrent Neural Networks 11 Bayesian Neural Networks 12 Ensemble Methods 13 Assignment of Final Projects Evaluation &amp; Workload\u200b Semester Workload Evaluation Methods Activity Hours Lectures 39 Bibliographic Assignment 31 Project Implementation 25 Independent Study 30 Course Total 125 Quiz Online quizzes by week or module Individual Project Final Individual Project Presentation Team Project Final Team Project Presentation ActivityHoursLectures39 Bibliographic Assignment 31 Project Implementation 25 Independent Study 30Course Total125 Quiz Online quizzes by week or module Individual Project Final Individual Project Presentation Team Project Final Team Project Presentation Bibliography Recommended Textbooks Scientific Journals Machine Learning, Kleidarithmos Publications, 2019Diamantaras &amp; Botsis Artificial Neural Networks, Kleidarithmos, 2007K. Diamantaras Pattern Recognition and Machine Learning, Fountas PublicationsChristopher Bishop Neural Networks and Machine Learning, Papasotiriou PublicationsSimon Haykin Introduction to Data Mining \u2014 Pearson Education, 2014Tan, Steinbach, Kumar JMLR Journal of Machine Learning Research IEEE TPAMIIEEE Trans. on Pattern Analysis &amp; Machine Intelligence IEEE TNNLSIEEE Trans. on Neural Networks and Learning Systems ESWA Expert Systems with Applications Neurocomputing Neurocomputing \u2014 Elsevier Machine Learning, Kleidarithmos Publications, 2019Diamantaras &amp; BotsisArtificial Neural Networks, Kleidarithmos, 2007K. DiamantarasPattern Recognition and Machine Learning, Fountas PublicationsChristopher BishopNeural Networks and Machine Learning, Papasotiriou PublicationsSimon HaykinIntroduction to Data Mining \u2014 Pearson Education, 2014Tan, Steinbach, KumarJMLR Journal of Machine Learning ResearchIEEE TPAMIIEEE Trans. on Pattern Analysis &amp; Machine IntelligenceIEEE TNNLSIEEE Trans. on Neural Networks and Learning SystemsESWA Expert Systems with ApplicationsNeurocomputing Neurocomputing \u2014 Elsevier Course Information Semester \u0391\u0384 ECTS5 Minutes per Week 180 Type Specialized Knowledge Requirements \u2014 Course Format Synchronous 30% Asynchronous 70% Remote e-class Erasmus \u2713 Technologies &amp; Tools Python scikit-learn TensorFlow Keras PyTorch XGBoost Random Forest SVM KNN Back to Courses Page \u03a0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b1 e-class \u03a5\u03bb\u03b9\u03ba\u03cc \u03bc\u03b1\u03b8\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2, \u03b2\u03af\u03bd\u03c4\u03b5\u03bf, forum &amp; \u03b1\u03bd\u03b1\u03ba\u03bf\u03b9\u03bd\u03ce\u03c3\u03b5\u03b9\u03c2 \u03a0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03c4\u03bf e-class"}