{"id":7,"date":"2014-03-20T19:40:49","date_gmt":"2014-03-20T19:40:49","guid":{"rendered":"http:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/?page_id=7"},"modified":"2024-09-24T08:55:48","modified_gmt":"2024-09-24T13:55:48","slug":"inicio","status":"publish","type":"page","link":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/","title":{"rendered":"Inicio"},"content":{"rendered":"<h1>2017-18<\/h1>\n<div class=\"table-responsive\"><table  style=\"width:100%; \"  class=\"easy-table easy-table-default \" border=\"0\">\n<thead>\r\n<tr><th style=\"background:#006772; color:#FFFFFF\">Nombre del curso:<\/th>\n<th > Intelligent systems architecture<\/th>\n<\/tr>\n<\/thead>\n<tbody>\r\n<tr><td style=\"background:#7FB3B8; color:#FFFFFF; font-style:italic\">Course Name:<\/td>\n<td > Intelligent systems architecture<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#006772; color:#FFFFFF\">Cr\u00e9ditos:<\/td>\n<td >\u00a04<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#7FB3B8; color:#FFFFFF\">Profesor:<\/td>\n<td > \u00a0Mar\u00eda de Arteaga &#8211; Carnegie Mellon University<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#006772; color:#FFFFFF\"><\/td>\n<td >\u00a0Hern\u00e1n Astudillo Ph.D. &#8211;\u00a0Universidad T\u00e9cnica Federico Santa Mar\u00eda &#8211; Chile<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#7FB3B8; color:#FFFFFF\"><\/td>\n<td > \u00a0Artur Dubrawski &#8211; Carnegie Mellon University<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#006772; color:#FFFFFF\"><\/td>\n<td >\u00a0\u00c1lvaro Riascos Ph.D &#8211; Universidad de los Andes<\/td>\n<\/tr>\n<\/tbody><\/table><\/div>\n\n<div class=\"arconix-tabs-horizontal\"><ul class=\"arconix-tabs\"><li data-arconix-icon=\"\" data-arconix-color=\"\" class=\"arconix-tab tab-Validezdelcurso\"><a class=\"\" href=\"#tab-Validezdelcurso\">Validez del curso<\/a><\/li>\n<li data-arconix-icon=\"\" data-arconix-color=\"\" class=\"arconix-tab tab-Description\"><a class=\"\" href=\"#tab-Description\">Description<\/a><\/li>\n<li data-arconix-icon=\"\" data-arconix-color=\"\" class=\"arconix-tab tab-Maingoals\"><a class=\"\" href=\"#tab-Maingoals\">Main goals<\/a><\/li>\n<li data-arconix-icon=\"\" data-arconix-color=\"\" class=\"arconix-tab tab-Coursestructure\"><a class=\"\" href=\"#tab-Coursestructure\">Course structure<\/a><\/li><\/ul>\n<div class=\"arconix-panes\"><div class=\"arconix-pane pane-Validezdelcurso\">\n<div class=\"table-responsive\"><table style=\"width:100%\" class=\"easy-table easy-table-default \" border=\"0\">\n<thead>\r\n<tr><th>Programa del\u00a0DISC:<\/th>\n<th> V\u00e1lido por:<\/th>\n<\/tr>\n<\/thead>\n<tbody>\r\n<tr><td style=\"background:#006772;color:#FFFFFF;font-style:bold\">MATI <\/td>\n<td> Curso de profundizaci\u00f3n<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#b9c69c;color:#1c1c1a;font-style:bold\">Otras maestr\u00edas<\/td>\n<td> Curso electivo<\/td>\n<\/tr>\n\r\n<tr><td style=\"background:#50BFD5;color:#FFFFFF;font-style:bold\">Pregrado <\/td>\n<td> Electiva profesional<\/td>\n<\/tr>\n<\/tbody><\/table><\/div><br \/>\n<\/div>\n<div class=\"arconix-pane pane-Description\">\n<h1>Description<\/h1>\n<p>Modern enterprise information systems involve the use of structured and unstructured data to support decision making, and optimize business processes. To tackle these new challenges, it is necessary to apply novel analytic and technological mechanisms to support the design, build, test, and deployment of intelligent systems.<\/p>\n<p>In this context, IT architects face new challenges. First, they need to interact with new stakeholders (i.e. data scientists) to understand architectural requirements related to big data ecosystems, data intensive algorithms and scalable architectures to achieve robust, flexible, useful and cost-effective solutions.<\/p>\n<p>This course presents three complementary modules, relevant to modern IT architects. First, intelligent systems must be designed and developed following new architectural approaches. Experimental software engineering is also presented as a mechanism to validate analytical solutions. Second, foundations of statistical learning are presented to understand the main tasks and requirements faced by data scientists. Third, advanced analytics concepts and real world problems in the domain of intelligent systems are presented. In this module students will apply the concepts learned in the previous two modules to propose architectures designed to handle large scale data applications.<br \/>\n<\/div>\n<div class=\"arconix-pane pane-Maingoals\">\n<h1>At the end of this course, you will be able to:<\/h1>\n<ul>\n<li>Summarize the features and value of experimental software engineering and software architecture in analytical projects<\/li>\n<li>Define and validate non-functional requirements in the context of Intelligent Systems architectures<\/li>\n<li>Describe the foundations of machine learning and its uses in Intelligent Systems solutions.<\/li>\n<li>Analyze and compare different scenarios of applied data science in Intelligent Systems through pragmatic case studies.<\/li>\n<li>Integrate acquired knowledge to architecting Intelligent Systems<\/li>\n<\/ul>\n<\/div>\n<div class=\"arconix-pane pane-Coursestructure\">\n<p><strong><u>I &#8211; Experimental Software Engineering and Architecture Design<\/u><\/strong><\/p>\n<p>Prof. Hern\u00e1n Astudillo<\/p>\n<p>Session 1 (6\/12\/2017):<\/p>\n<ul>\n<li>Software architecture: motivation, process, description, quality, reference architectures, ecosystems.<\/li>\n<li>Architecture process: drivers, core ideas, structure, traceability.<\/li>\n<li>Case study: SNAM v1 as-is\/to-be.<\/li>\n<\/ul>\n<p>Session 2 (6\/13\/2017):<\/p>\n<ul>\n<li>Architecture elaboration: knowledge reuse \u2013 components, frameworks, styles, patterns, tactics.<\/li>\n<li>Knowledge construction: experimental software engineering, case studies, systematic reviews, experiments.<\/li>\n<li>Architectural knowledge: decisions capture, presentation, sharing, reuse.<\/li>\n<li>Case: PUA designers decision process (using DVIA).<\/li>\n<li>Case study: SNAM v2 to-be (reusing knowledge).<\/li>\n<\/ul>\n<p>Session 3 (6\/14\/2017):<\/p>\n<ul>\n<li>Quality attributes (non-functional requirements): performance, availability, scalability.<\/li>\n<li>Architecture evaluation: quality models, trade-offs, <em>softgoals<\/em>, SAAM, ATAM.<\/li>\n<li>Case study: SNAM v3 to-be (quality-evaluated).<\/li>\n<li>Case: ExoBanco.<\/li>\n<\/ul>\n<p>Session 4 (6\/15\/2017):<\/p>\n<ul>\n<li>Case study: MySpace \u2013 drivers, alternatives, trade-offs.<\/li>\n<li>Case study: big data architectures \u2013 drivers, alternatives, trade-offs, reference architectures.<\/li>\n<\/ul>\n<p><strong><u>II \u2013 Fundamentals of Machine Learning<\/u><\/strong><\/p>\n<p>Prof. Alvaro Riascos<\/p>\n<p>Session 1 (6\/20\/2017):<\/p>\n<ul>\n<li>Introduction to statistical learning: risk, approximation and estimation error, overfitting, optimal regression and classification algorithms, bounds, consistency, no free lunch theorems. References: [JWHT], Chapter 1. [HTF], Chapter 1. [LS]. [JWHT], Sections 2.1, 2.2.<\/li>\n<li>Linear methods of regression and classification and regularization. References:[JWHT], Chapter 3,4. [HTF], Chapter 3,4.<\/li>\n<\/ul>\n<p>Session 2 (6\/21\/2017):<\/p>\n<ul>\n<li>Non-linear methods and regularization. [HTF]: Chapter 5,6.<\/li>\n<li>Trees, boosting and random forests. [HTF]: Chapter 9,10,15.<\/li>\n<\/ul>\n<p>Session 3 (6\/22\/2017):<\/p>\n<ul>\n<li>Neural networks and an introduction to deep learning. [B]: Chapter 5. [DLB]: Selected topics.<\/li>\n<\/ul>\n<p><strong><u>III- Intelligent Systems<\/u><\/strong><\/p>\n<p>Profs. Artur Dubrawski , Mar\u00eda De Arteaga<\/p>\n<p>Session 1 (6\/28\/2017):<\/p>\n<ul>\n<li>Engineering and characterization of data: Bringing data to shape for analysis. Clustering, characterizing, and summarizing data.<\/li>\n<\/ul>\n<p>Session 2 (6\/29\/2017):<\/p>\n<ul>\n<li>Modeling expectations. Detecting anomalies and anomalous patterns.<\/li>\n<li>Case studies: surveillance of public health, food safety, breaks of billing patterns in health insurance.<\/li>\n<\/ul>\n<p>Session 3 (6\/30\/2017):<\/p>\n<ul>\n<li>Learning from multi-modal data: How to learn from text, images, videos, and sounds.<\/li>\n<\/ul>\n<p>Session 4 (7\/4\/2017):<\/p>\n<ul>\n<li>Graphical models: Probabilities, Bayesian statistics, graphical models.<\/li>\n<\/ul>\n<p>Session 5 (7\/5\/2017):<\/p>\n<ul>\n<li>Learning with scarce labels: Active learning, semi-supervised learning, transfer learning.<\/li>\n<li>How things can go wrong: Non-iid data, correlation is not causation, unfair algorithms.<\/li>\n<\/ul>\n<p><u><\/u><\/div><\/div><\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>2017-18 &hellip; <a class=\"more-link\" href=\"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/\">Sigue leyendo <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":91,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/pages\/7","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/users\/91"}],"replies":[{"embeddable":true,"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/comments?post=7"}],"version-history":[{"count":38,"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/pages\/7\/revisions"}],"predecessor-version":[{"id":143,"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/pages\/7\/revisions\/143"}],"wp:attachment":[{"href":"https:\/\/cursos.virtual.uniandes.edu.co\/arti4211\/wp-json\/wp\/v2\/media?parent=7"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}