Intelligent Systems Architecture

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.

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.

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.

Fecha:

Curso: 12 de Junio al 5 de Julio

A

Idioma:

Inglés - Español

Profesor:

Hernán Astudillo Ph.D, Álvaro Riascos Ph.D, Artur Dubrawski. Ph.D. y María De Arteaga

Horario: 

 Módulo I: 12, 13 y 14 de junio (de 5 p.m. a 9 p.m.) y 15 de junio (de 2 p.m. a 6 p.m.) - profesor Hernán Astudillo Ph.D.

 Módulo II: 20, 21 y 22 de junio (de 6 p.m. a 9 p.m.) - profesor Álvaro Riascos Ph.D.

 Módulo III: 28, 29, 30 de junio y 4 y 5 de julio (de 5 p.m. a 9 p.m.) - profesores Artur Dubrawski. Ph.D. y María De Arteaga

 

Domingo 11	Lunes 12	Martes 13	Miércol. 14	Jueves 15	Viernes 16	Sábado 17
Domingo 18	Lunes 19	Martes 20	Miércol. 21	Jueves 22	Viernes 23	Sábado 24
Domigo 25	Lunes 26	Martes 27	Miércol. 28	Jueves 29	Viernes 30	Sábado 1
Domigo 2	Lunes 3	Martes 4	Miércol. 5	Jueves 6	Viernes 7	Sábado 8

 

6:00 p.m - 9:00 p.m

5:00 p.m. - 9:00 p.m.

2:00 p.m - 6:00 p.m

Estudiantes MATI:

25

Otros Estudiantes:

10

Estudiantes MATI:

Curso de profundización

Estudiantes otras maestrías:

Curso electivo

Estudiantes ISIS:

Electiva profesional

Institución: Carnegie Mellon University

Institución: Universidad Técnica Federico Santa María – Chile

Institución: Carnegie Mellon University

Hoja de vida

Artur Dubrawski received a Ph.D. in robotics and automation from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and a M.Sc. in aircraft engineering from Warsaw University of Technology. Artur considers himself a scientist and a practitioner; he has been tainted with some real world entrepreneurial experiences. He had started up a small company which turned out successful in integration and deployment of advanced computerized control systems and novel technological devices. He had also been affiliated with startups incorporated by others: Schenley Park Research, a data mining consultancy and a CMU spin-off, where he was a scientist; and (more recently) with Aethon, a company building robots to automate transportation in hospitals, where he served as a Chief Technical Officer. Artur returned to CMU in 2003 to join the Robotics Institute's Auton Lab. He works on a range of applied data mining endeavors and teaches data mining to graduate students at the CMU Heinz School (in fact, he has been doing that since 2000). In his previous academic life, he worked mainly on machine learning approaches to mobile robot navigation and control, as well as on other applications of adaptive autonomous systems. In 1995/96 Artur spent a year at CMU (obviously, with the Auton Lab) as a visiting Fulbright scholar. In January 2006 Artur Dubrawski was named the director of the Auton Lab.

Institución: Universidad de los Andes