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Oscar Germán Duarte Velasco

Los sistemas difusos permiten efectuar cálculos cuando hay información con incertidumbre, o cuando se debe combinar información tanto cuantitativa como cualitativa. Se trata de una aproximación matemática para modelar esas situaciones. Este curso está diseñado para ayudar a entender y explicar cómo funcionan dichos sistemas.

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Los sistemas difusos permiten efectuar cálculos cuando hay información con incertidumbre, o cuando se debe combinar información tanto cuantitativa como cualitativa. Se trata de una aproximación matemática para modelar esas situaciones. Este curso está diseñado para ayudar a entender y explicar cómo funcionan dichos sistemas.

El curso tiene una aproximación teórica y práctica. Los principios matemáticos son de un nivel bajo y están al alcance de un público muy amplio. El curso cuenta con varios laboratorios para aprender a utilizar las herramientas de software que usan esos principios. Este componente práctico requiere una comprensión mínima de programación.

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Syllabus

Teoría de conjuntos difusos
En esta semana se presentan los principios básicos de la teoría de conjuntos difusos y sus operaciones. También se explica cómo pueden usarse para ayudar a representar algunas situaciones en las que la información es imperfecta. Recuerde que también puede consultar el ítem recursos durante todo el curso, allí puede encontrar información de utilidad.
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Lógica difusa y razonamiento aproximado
En esta semana se presentan los sistemas difusos más conocidos. Se denominan 'Sistemas Basados en Reglas', o también 'Controladores Difusos'. Estos sistemas implementan una forma de razonamiento aproximado que se basa en la lógica difusa. En esta semana se muestran también varias herramientas de software.
Aprendizaje de máquina
En esta semana se muestran algunos ejemplos sobre cómo se pueden usar las estrategias de aprendizaje de máquina para diseñar u optimizar los sistemas basados en reglas, o algunos de sus componentes.
Aritmética difusa
En esta semana se presentan los ''Sistemas de Computación con Palabras basados en Aritmética Difusa'. Con este tipo de sistemas se pueden modelar situaciones complejas, de un elevado número de variables, sin incurrir en el problema de explosión de la base de reglas.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines fuzzy sets and fuzzy logic, key concepts in building intelligent systems
Offers hands-on labs and interactive materials for practical application
Introduces fuzzy arithmetic, a powerful tool for modeling complex systems
Requires a basic understanding of programming for the practical component

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Sistemas difusos with these activities:
Elaborar un diagrama de flujo o mapa mental de los conceptos de sistemas difusos
Resumir y visualizar los conceptos clave de los sistemas difusos para mejorar la comprensión.
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  • Identificar los conceptos principales de los sistemas difusos.
  • Organizar los conceptos en un diagrama de flujo o mapa mental.
  • Añadir ejemplos y explicaciones para aclarar los conceptos.
Participar en sesiones de estudio y práctica con compañeros
Colaborar con otros estudiantes para revisar los conceptos y resolver problemas juntos.
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  • Formar un grupo de estudio de 2 o 3 personas.
  • Programar sesiones de estudio regulares.
  • Revisar los materiales del curso juntos.
  • Resolver problemas y ejercicios prácticos.
Show all two activities

Career center

Learners who complete Sistemas difusos will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that can be used to solve problems. The 'Machine Learning' module in this course can help you develop the skills you need to succeed in this role, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Data Scientist
Data Scientists are experts in using data to solve problems, and they often use statistical techniques and machine learning algorithms to do this. The 'Machine Learning' module in this course can help you develop the skills you need to succeed in this role, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses and organizations make better decisions. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Financial Analyst
Financial Analysts help businesses and organizations make informed decisions about their finances. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Risk Analyst
Risk Analysts identify and assess risks that businesses and organizations face. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Market Research Analyst
Market Research Analysts help businesses and organizations understand their customers and markets. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Insurance Analyst
Insurance Analysts help businesses and individuals assess and manage their insurance risks. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Transportation Analyst
Transportation Analysts help businesses and organizations improve their transportation systems. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Healthcare Analyst
Healthcare Analysts help healthcare providers and organizations improve their performance and efficiency. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Business Analyst
Business Analysts help businesses and organizations improve their performance by analyzing their operations and suggesting improvements. The 'Machine Learning' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to use machine learning strategies to design or optimize systems based on rules or some of their components.
Software Engineer
Software Engineers design, develop, and maintain software systems. The 'Aritmética difusa' module in this course may be useful to you if you want to pursue a career in this field, as it discusses how to model complex situations with a large number of variables without encountering the problem of rule base explosion.

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Sistemas difusos.
Este libro ofrece una cobertura avanzada de las aplicaciones de la lógica difusa en ingeniería, incluyendo control, robótica, optimización y sistemas de toma de decisiones.
Este libro proporciona una visión completa de los sistemas difusos, con un enfoque en aplicaciones de ingeniería. Ofrece una base teórica sólida y ejemplos prácticos relevantes para el curso.
Este libro proporciona una guía integral para utilizar la caja de herramientas de lógica difusa de MATLAB para desarrollar y simular sistemas difusos. Es un recurso valioso para aquellos que trabajan con MATLAB en el campo de la lógica difusa.
Este libro es una introducción accesible a la lógica difusa para principiantes. Proporciona una visión general de los conceptos básicos y ejemplos de aplicaciones prácticas.
Este libro clásico establece los fundamentos teóricos de la lógica difusa. Ofrece una base sólida para comprender los conceptos y principios subyacentes al material del curso.
Este libro ofrece una introducción completa a los sistemas difusos, cubriendo tanto los aspectos teóricos como prácticos. Proporciona una base sólida para comprender el material del curso.
Este libro se enfoca en técnicas de aprendizaje profundo, que son tangenciales al material del curso. Sin embargo, ofrece información valiosa sobre algoritmos de aprendizaje de última generación que pueden complementar la comprensión del curso.

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