{"id":17100,"date":"2024-06-26T14:49:54","date_gmt":"2024-06-26T07:49:54","guid":{"rendered":"http:\/\/www.mtec.or.th\/en\/?p=17100"},"modified":"2024-06-26T15:07:23","modified_gmt":"2024-06-26T08:07:23","slug":"general-training-courses-71002","status":"publish","type":"post","link":"https:\/\/www.mtec.or.th\/en\/general-training-courses-71002\/","title":{"rendered":"\u0e2b\u0e25\u0e31\u0e01\u0e2a\u0e39\u0e15\u0e23\u0e2d\u0e1a\u0e23\u0e21\u0e40\u0e0a\u0e34\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23 Machine Learning and Data Analysis for Materials Scientists and Engineers (\u0e01\u0e22. \u2013 \u0e1e\u0e22. 65)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"17100\" class=\"elementor elementor-17100\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3724ef7 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"3724ef7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4ed5d3e\" data-id=\"4ed5d3e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd48dce elementor-widget elementor-widget-heading\" data-id=\"cd48dce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">\u0e2b\u0e25\u0e31\u0e01\u0e2a\u0e39\u0e15\u0e23\u0e2d\u0e1a\u0e23\u0e21\u0e40\u0e0a\u0e34\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23 Machine Learning and Data Analysis for Materials Scientists and Engineers (\u0e01\u0e22. \u2013 \u0e1e\u0e22. 65)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ae2dfe2 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"ae2dfe2\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-251df94\" data-id=\"251df94\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c8ffb0b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c8ffb0b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-566d3b8\" data-id=\"566d3b8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-a8809e6\" data-id=\"a8809e6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-98c942c elementor-widget elementor-widget-text-editor\" data-id=\"98c942c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 18pt;\">\u0e2b\u0e25\u0e31\u0e01\u0e2a\u0e39\u0e15\u0e23\u0e1d\u0e36\u0e01\u0e2d\u0e1a\u0e23\u0e21\u0e40\u0e0a\u0e34\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23<br \/><\/span><br \/><strong><span style=\"font-size: 18pt;\">Machine Learning and Data Analysis for Materials Scientists and Engineers<\/span><\/strong><br \/><span style=\"font-size: 14pt;\"><br \/>\u0e40\u0e23\u0e35\u0e22\u0e19 Online (Zoom platform) \u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07 \u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19 \u2013 \u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19 2565<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e2d\u0e19\u0e42\u0e14\u0e22 \u0e17\u0e35\u0e21\u0e1c\u0e39\u0e49\u0e40\u0e0a\u0e35\u0e48\u0e22\u0e27\u0e0a\u0e32\u0e0d\u0e14\u0e49\u0e32\u0e19 Machine Learning \u0e41\u0e25\u0e30 Deep Learning<\/span><br \/><span style=\"font-size: 14pt;\">\u0e19\u0e33\u0e42\u0e14\u0e22 \u0e14\u0e23. \u0e2a\u0e34\u0e23\u0e30 \u0e28\u0e23\u0e35\u0e2a\u0e27\u0e31\u0e2a\u0e14\u0e34\u0e4c \u0e1d\u0e48\u0e32\u0e22\u0e27\u0e34\u0e08\u0e31\u0e22 \u0e04\u0e13\u0e30\u0e41\u0e1e\u0e17\u0e22\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c \u0e08\u0e38\u0e2c\u0e32\u0e25\u0e07\u0e01\u0e23\u0e13\u0e4c\u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22 \u0e41\u0e25\u0e30 \u0e14\u0e23. \u0e2d\u0e34\u0e17\u0e18\u0e34 \u0e09\u0e31\u0e15\u0e23\u0e19\u0e31\u0e19\u0e17\u0e40\u0e27\u0e0a Nanoinformatics and Artificial Intelligence Research Team \u0e28\u0e39\u0e19\u0e22\u0e4c\u0e19\u0e32\u0e42\u0e19\u0e40\u0e17\u0e04\u0e42\u0e19\u0e42\u0e25\u0e22\u0e35\u0e41\u0e2b\u0e48\u0e07\u0e0a\u0e32\u0e15\u0e34<\/span><\/p><p style=\"text-align: center;\"><a href=\"http:\/\/www.mtec.or.th\/en\/wp-content\/uploads\/2024\/06\/brochure-A4_270865_compressed-2-1.pdf\" target=\"_blank\" rel=\"noopener\"><span style=\"font-size: 14pt;\">&lt;\u0e14\u0e32\u0e27\u0e19\u0e4c\u0e42\u0e2b\u0e25\u0e14\u0e42\u0e1b\u0e2a\u0e40\u0e15\u0e2d\u0e23\u0e4c\u0e1b\u0e23\u0e30\u0e0a\u0e32\u0e2a\u0e31\u0e21\u0e1e\u0e31\u0e19\u0e18\u0e4c&gt;<\/span><\/a><\/p><p><strong><span style=\"font-size: 14pt;\">Track A: Machine Learning Principles and Communication<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">\u0e23\u0e31\u0e1a\u0e44\u0e21\u0e48\u0e08\u0e33\u0e01\u0e31\u0e14\u0e08\u0e33\u0e19\u0e27\u0e19 | \u0e23\u0e30\u0e22\u0e30\u0e40\u0e27\u0e25\u0e32\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19 7 \u0e0a\u0e31\u0e48\u0e27\u0e42\u0e21\u0e07 (\u0e01\u0e31\u0e19\u0e22\u0e32\u0e22\u0e19 2565) | \u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19\u0e23\u0e39\u0e1b\u0e41\u0e1a\u0e1a\u0e02\u0e2d\u0e07 Lecture<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e01\u0e33\u0e2b\u0e19\u0e14\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19 Track A:<\/span><\/strong><\/p><p><span style=\"font-size: 14pt;\">\u2022 Introduction to machine learning and deep learning (5 \u0e01.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of unsupervised techniques (12 \u0e01.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of supervised techniques (19 \u0e01.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Machine learning experimental design (26 \u0e01.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Course wrap-up and discussion (28 \u0e01.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 10:00-11:00 \u0e19.)<\/span><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e2a\u0e34\u0e48\u0e07\u0e17\u0e35\u0e48\u0e1c\u0e39\u0e49\u0e40\u0e23\u0e35\u0e22\u0e19\u0e08\u0e30\u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a:<\/span><\/strong><\/p><p><span style=\"font-size: 14pt;\">\u0e40\u0e02\u0e49\u0e32\u0e43\u0e08\u0e2b\u0e25\u0e31\u0e01\u0e01\u0e32\u0e23\u0e41\u0e25\u0e30\u0e02\u0e35\u0e14\u0e04\u0e27\u0e32\u0e21\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e02\u0e2d\u0e07 Machine Learning \u0e41\u0e25\u0e30 Deep Learning<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e27\u0e34\u0e08\u0e31\u0e22\u0e17\u0e35\u0e48\u0e2d\u0e32\u0e28\u0e31\u0e22\u0e01\u0e32\u0e23\u0e1e\u0e31\u0e12\u0e19\u0e32 Machine Learning Model \u0e44\u0e14\u0e49<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2a\u0e37\u0e48\u0e2d\u0e2a\u0e32\u0e23\u0e01\u0e31\u0e1a\u0e19\u0e31\u0e01\u0e27\u0e34\u0e08\u0e31\u0e22\u0e17\u0e32\u0e07 Machine Learning \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e07\u0e32\u0e19\u0e27\u0e34\u0e08\u0e31\u0e22\u0e23\u0e48\u0e27\u0e21\u0e01\u0e31\u0e19\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e21\u0e35\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e<\/span><br \/><strong><span style=\"font-size: 14pt;\">\u0e2b\u0e31\u0e27\u0e02\u0e49\u0e2d\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19 Track A:<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">1.Principles of classical machine learning<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Optimization view of ML<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Model development<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Post-development<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.Deployment<\/span><br \/><strong><span style=\"font-size: 14pt;\">2.Principles of unsupervised techniques<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Dimensionality reduction<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Clustering<\/span><br \/><strong><span style=\"font-size: 14pt;\">3.Principles of supervised techniques<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Linear model<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.SVM<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Tree model<\/span><br \/><strong><span style=\"font-size: 14pt;\">4.Principles of deep learning<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Artificial neural network<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.End-to-end learning and representation learning<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Convolutional neural networks<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.Recurrent neural networks<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 5.Graph neural network<\/span><br \/><strong><span style=\"font-size: 14pt;\">5.Machine learning experimental design<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">6.Communications<\/span><\/strong><\/p><p><span style=\"font-size: 14pt;\">Track B: Data Science and Machine Learning Practical Skills<\/span><\/p><p><strong><span style=\"font-size: 14pt;\">**\u0e23\u0e31\u0e1a\u0e08\u0e33\u0e19\u0e27\u0e19\u0e08\u0e33\u0e01\u0e31\u0e14 \u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e1c\u0e39\u0e49\u0e17\u0e35\u0e48\u0e2a\u0e19\u0e43\u0e08\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19\u0e40\u0e0a\u0e34\u0e07\u0e25\u0e36\u0e01\u0e41\u0e25\u0e30\u0e1d\u0e36\u0e01\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23**<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">\u0e23\u0e30\u0e22\u0e30\u0e40\u0e27\u0e25\u0e32\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19 19 \u0e0a\u0e31\u0e48\u0e27\u0e42\u0e21\u0e07 (\u0e15\u0e38\u0e25\u0e32\u0e04\u0e21 \u2013 \u0e1e\u0e24\u0e28\u0e08\u0e34\u0e01\u0e32\u0e22\u0e19 2565) | \u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19\u0e23\u0e39\u0e1b\u0e41\u0e1a\u0e1a Lecture &amp; Hands-on<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">\u0e23\u0e30\u0e22\u0e30\u0e40\u0e27\u0e25\u0e32\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e1e\u0e37\u0e49\u0e19\u0e10\u0e32\u0e19 Python Programming \u0e14\u0e49\u0e27\u0e22\u0e15\u0e19\u0e40\u0e2d\u0e07 [Pre-course]: 18 \u0e0a\u0e31\u0e48\u0e27\u0e42\u0e21\u0e07 [4 Kaggle course]<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">**\u0e21\u0e35\u0e01\u0e32\u0e23\u0e2a\u0e2d\u0e1a\u0e27\u0e31\u0e14\u0e1e\u0e37\u0e49\u0e19\u0e10\u0e32\u0e19\u0e14\u0e49\u0e32\u0e19 Python programming**<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e01\u0e33\u0e2b\u0e19\u0e14\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19 Track B:<\/span><\/strong><\/p><p><span style=\"font-size: 14pt;\">\u2022 Statistical inference and imputation (5 \u0e15.\u0e04. 65 \u0e40\u0e27\u0e25\u0e32 10:30-12:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Introduction to machine learning and deep learning (10 \u0e15.\u0e04. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of unsupervised techniques (17 \u0e15.\u0e04. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Unsupervised learning practice (19 \u0e15.\u0e04. 65 \u0e40\u0e27\u0e25\u0e32 10:30-12:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of supervised techniques (26 \u0e15.\u0e04., 10:30-12:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Supervised learning practice I (31 \u0e15.\u0e04. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Supervised learning practice II (7 \u0e1e.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-10:30 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of deep learning I (14 \u0e1e.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-11:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Principles of deep learning II (21 \u0e1e.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-11:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Deep learning practice I (28 \u0e1e.\u0e22. 65 \u0e40\u0e27\u0e25\u0e32 09:00-11:00 \u0e19.)<\/span><br \/><span style=\"font-size: 14pt;\">\u2022 Deep learning practice II (7 \u0e18.\u0e04. 65 \u0e23\u0e30\u0e22\u0e30\u0e40\u0e27\u0e25\u0e32 2 \u0e0a\u0e21.)<\/span><\/p><p><span style=\"font-size: 14pt;\">\u0e2a\u0e34\u0e48\u0e07\u0e17\u0e35\u0e48\u0e1c\u0e39\u0e49\u0e40\u0e23\u0e35\u0e22\u0e19\u0e08\u0e30\u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a:<\/span><\/p><p><span style=\"font-size: 14pt;\">\u0e40\u0e02\u0e49\u0e32\u0e43\u0e08\u0e2b\u0e25\u0e31\u0e01\u0e01\u0e32\u0e23\u0e41\u0e25\u0e30\u0e02\u0e35\u0e14\u0e04\u0e27\u0e32\u0e21\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e02\u0e2d\u0e07 Machine Learning \u0e41\u0e25\u0e30 Deep Learning<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e42\u0e1b\u0e23\u0e41\u0e01\u0e23\u0e21\u0e20\u0e32\u0e29\u0e32 Python \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e01\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e21\u0e27\u0e25\u0e1c\u0e25\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25 \u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e32\u0e07\u0e2a\u0e16\u0e34\u0e15\u0e34 \u0e41\u0e25\u0e30\u0e41\u0e2a\u0e14\u0e07\u0e1c\u0e25\u0e40\u0e1b\u0e47\u0e19\u0e01\u0e23\u0e32\u0e1f\u0e17\u0e35\u0e48\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e21\u0e15\u0e48\u0e2d\u0e01\u0e32\u0e23\u0e2a\u0e37\u0e48\u0e2d\u0e2a\u0e32\u0e23\u0e1c\u0e25\u0e01\u0e32\u0e23\u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e40\u0e17\u0e04\u0e19\u0e34\u0e04\u0e04\u0e31\u0e14\u0e40\u0e25\u0e37\u0e2d\u0e01\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e41\u0e25\u0e30\u0e1b\u0e23\u0e31\u0e1a\u0e1e\u0e32\u0e23\u0e32\u0e21\u0e34\u0e40\u0e15\u0e2d\u0e23\u0e4c\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e43\u0e2b\u0e49\u0e44\u0e14\u0e49\u0e42\u0e21\u0e40\u0e14\u0e25\u0e17\u0e35\u0e48\u0e21\u0e35\u0e04\u0e27\u0e32\u0e21\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e15\u0e32\u0e21\u0e40\u0e1b\u0e49\u0e32\u0e2b\u0e21\u0e32\u0e22<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e17\u0e33\u0e04\u0e27\u0e32\u0e21\u0e40\u0e02\u0e49\u0e32\u0e43\u0e08\u0e41\u0e25\u0e30\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c\u0e43\u0e0a\u0e49\u0e01\u0e23\u0e30\u0e1a\u0e27\u0e19\u0e01\u0e32\u0e23\u0e1e\u0e31\u0e12\u0e19\u0e32 Machine Learning Model \u0e08\u0e32\u0e01\u0e1a\u0e17\u0e04\u0e27\u0e32\u0e21\u0e15\u0e35\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e44\u0e14\u0e49<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e19\u0e33 Artificial Neural Network Model \u0e17\u0e35\u0e48\u0e16\u0e39\u0e01\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a\u0e44\u0e27\u0e49\u0e41\u0e25\u0e49\u0e27\u0e21\u0e32 train \u0e1a\u0e19\u0e0a\u0e38\u0e14\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e02\u0e2d\u0e07\u0e15\u0e19\u0e40\u0e2d\u0e07\u0e44\u0e14\u0e49<\/span><br \/><span style=\"font-size: 14pt;\">\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a\u0e41\u0e25\u0e30\u0e14\u0e31\u0e14\u0e41\u0e1b\u0e25\u0e07 Artificial Neural Network Model \u0e1e\u0e37\u0e49\u0e19\u0e10\u0e32\u0e19\u0e44\u0e14\u0e49<\/span><br \/><strong><span style=\"font-size: 14pt;\">\u0e2b\u0e31\u0e27\u0e02\u0e49\u0e2d\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19 Track B:<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">1.[Pre-course] Kaggle\u2019s Python programming, data handling, and data visualization<\/span><\/strong><br \/><span style=\"font-size: 14pt;\"><strong>2.Statistical inference and imputation [Lecture \/ Hands on<\/strong>]<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Review of statistical techniques<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Statistical inference<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Imputation<\/span><br \/><strong><span style=\"font-size: 14pt;\">3.Principles of classical machine learning [Lecture, Joint with Track A]<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">4.Principles of unsupervised techniques [Lecture, Joint with Track A]<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">5.Unsupervised techniques practice [Hands on]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.PCA, PCoA (MDS)<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.t-SNE, UMAP<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.k-mean, hierarchical\/agglomerative<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.DBSCAN<\/span><br \/><strong><span style=\"font-size: 14pt;\">6.Principles of supervised techniques [Lecture, Joint with Track A]<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">7.Supervised techniques practice I \u2013 Model tuning [Hands on]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Linear and logistic regression<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Support vector machine<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Decision tree<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.Bagging: Random Forest<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 5.Boosting: AdaBoost, XGBoost<\/span><br \/><strong><span style=\"font-size: 14pt;\">8.Supervised techniques practice II \u2013 Full pipeline [Hands on]<\/span><\/strong><br \/><strong><span style=\"font-size: 14pt;\">9.Principles of deep learning I [Lecture]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Artificial neural network<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Learning process<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.End-to-end learning and representation learning view of DL<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.Convolutional neural networks<\/span><br \/><strong><span style=\"font-size: 14pt;\">10.Principles of deep learning II [Lecture]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Recurrent neural networks<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Attention mechanism and transformer architecture<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Graph neural network<\/span><br \/><strong><span style=\"font-size: 14pt;\">11.Practical deep learning I \u2013 Transfer learning [Hands on]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Transfer learning<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Data preprocessing<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.Training control<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.Saliency map<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 5.Representation learning<\/span><br \/><strong><span style=\"font-size: 14pt;\">12.Practical deep learning II \u2013 Building your own model [Hands on]<\/span><\/strong><br \/><span style=\"font-size: 14pt;\">\u00a0 1.Build a new model from scratch<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 2.Fully connected model<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 3.CNN model<\/span><br \/><span style=\"font-size: 14pt;\">\u00a0 4.RNN model<\/span><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e1f\u0e23\u0e35 \u0e44\u0e21\u0e48\u0e21\u0e35\u0e04\u0e48\u0e32\u0e43\u0e0a\u0e49\u0e08\u0e48\u0e32\u0e22 \u0e2a\u0e19\u0e43\u0e08\u0e2a\u0e21\u0e31\u0e04\u0e23\u0e40\u0e02\u0e49\u0e32\u0e23\u0e48\u0e27\u0e21\u0e01\u0e32\u0e23\u0e40\u0e23\u0e35\u0e22\u0e19\u0e23\u0e39\u0e49\u0e43\u0e19 Track A \u0e2b\u0e23\u0e37\u0e2d Track B \u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e2a\u0e41\u0e01\u0e19 QR Code<\/span><\/strong><\/p><p><img decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/www.mtec.or.th\/wp-content\/uploads\/2022\/08\/2MI_FundamentalRegisCode1-840x840.jpg\" width=\"194\" height=\"194\" \/><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e2b\u0e23\u0e37\u0e2d\u0e17\u0e35\u0e48\u0e25\u0e34\u0e07\u0e04\u0e4c https:\/\/forms.gle\/tGQ9fgtx6SgoSNry8<\/span><\/strong><\/p><p><strong><span style=\"font-size: 14pt;\">\u0e15\u0e34\u0e14\u0e15\u0e48\u0e2d\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e02\u0e2d\u0e23\u0e31\u0e1a\u0e23\u0e32\u0e22\u0e25\u0e30\u0e40\u0e2d\u0e35\u0e22\u0e14\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21\u0e44\u0e14\u0e49\u0e17\u0e35\u0e48 MTEC Materials Informatics Project Email: &#50;&#x6d;&#x69;&#64;&#109;&#x74;e&#99;&#x2e;&#x6f;&#114;&#x2e;&#x74;h<\/span><\/strong><\/p><p><em><span style=\"font-size: 14pt;\">\u0e01\u0e34\u0e08\u0e01\u0e23\u0e23\u0e21\u0e19\u0e35\u0e49\u0e2a\u0e19\u0e31\u0e1a\u0e2a\u0e19\u0e38\u0e19\u0e42\u0e14\u0e22\u0e2a\u0e33\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e1b\u0e25\u0e31\u0e14\u0e01\u0e23\u0e30\u0e17\u0e23\u0e27\u0e07\u0e01\u0e32\u0e23\u0e2d\u0e38\u0e14\u0e21\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e27\u0e34\u0e17\u0e22\u0e32\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c \u0e27\u0e34\u0e08\u0e31\u0e22\u0e41\u0e25\u0e30\u0e19\u0e27\u0e31\u0e15\u0e01\u0e23\u0e23\u0e21 (\u0e2a\u0e1b. \u0e2d\u0e27.) \u0e20\u0e32\u0e22\u0e43\u0e15\u0e49\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e01\u0e32\u0e23\u0e40\u0e2a\u0e23\u0e34\u0e21\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e28\u0e31\u0e01\u0e22\u0e20\u0e32\u0e1e\u0e41\u0e25\u0e30\u0e02\u0e31\u0e1a\u0e40\u0e04\u0e25\u0e37\u0e48\u0e2d\u0e19\u0e04\u0e27\u0e32\u0e21\u0e23\u0e48\u0e27\u0e21\u0e21\u0e37\u0e2d\u0e40\u0e0a\u0e34\u0e07\u0e22\u0e38\u0e17\u0e18\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c\u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07\u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e17\u0e27\u0e34\u0e20\u0e32\u0e04\u0e35\u0e41\u0e25\u0e30\u0e1e\u0e2b\u0e38\u0e20\u0e32\u0e04\u0e35 \u0e1b\u0e23\u0e30\u0e08\u0e33\u0e1b\u0e35\u0e07\u0e1a\u0e1b\u0e23\u0e30\u0e21\u0e32\u0e13 2565<\/span><\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-26e3c62\" data-id=\"26e3c62\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b85479f elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"b85479f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-04d18ac\" data-id=\"04d18ac\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5de461c elementor-widget elementor-widget-spacer\" data-id=\"5de461c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>\u0e2b\u0e25\u0e31\u0e01\u0e2a\u0e39\u0e15\u0e23\u0e2d\u0e1a\u0e23\u0e21\u0e40\u0e0a\u0e34\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23 Machine Learning and Data Analysis for Materials Scientists and Engineers (\u0e01\u0e22. \u2013 \u0e1e\u0e22. 65) &#8230; <a title=\"\u0e2b\u0e25\u0e31\u0e01\u0e2a\u0e39\u0e15\u0e23\u0e2d\u0e1a\u0e23\u0e21\u0e40\u0e0a\u0e34\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e01\u0e32\u0e23 Machine Learning and Data Analysis for Materials Scientists and Engineers (\u0e01\u0e22. \u2013 \u0e1e\u0e22. 65)\" 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