Sistemas difusos (Coursera)

Sistemas difusos (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

What You Will Learn

  • Conocerás una aproximación matemática sencilla que permite acercar las descripciones cualitativas y cuantitativas de fenómenos muy variados.
  • Podrás explicar cómo funcionan ciertos sistemas que permiten hacer cálculos a partir de una descripción con palabras del funcionamiento deseado.
  • Podrás explicar cómo funcionan ciertos sistemas capaces de obtener adjetivos que califican una variable, a partir de otros adjetivos.

Syllabus

WEEK 1
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.

WEEK 2
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.

WEEK 3
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.

WEEK 4
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Image Generation (Coursera) Coursera
Google Cloud

Introduction to Image Generation (Coursera)

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Nov 9th 2026
3 Weeks
Supply Chain Analytics (Coursera) Coursera
IIT Roorkee

Supply Chain Analytics (Coursera)

Welcome to Supply Chain Analytics! In this course you will learn about advanced decision problems in Supply Chain Management and the application of optimisation formulations and their solutions to address them. The course has been designed to help you advance your career as business analysts, supply chain managers, and other similar roles by learning in-demand skills to increase efficiency, drive organisational growth, and make a positive business impact. The course also offers a good starting point to those with purely academic and research interests.

Nov 16th 2026
5-12 Weeks
Matrix Factorization and Advanced Techniques (Coursera) Coursera
University of Minnesota

Matrix Factorization and Advanced Techniques (Coursera)

In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.

Nov 2nd 2026
5-12 Weeks
Applied Text Mining in Python (Coursera) Coursera
University of Michigan

Applied Text Mining in Python (Coursera)

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

Nov 9th 2026
4 Weeks
Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Nov 16th 2026
5-12 Weeks
Matrix Methods (Coursera) Coursera
University of Minnesota

Matrix Methods (Coursera)

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Nov 9th 2026
5-12 Weeks
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Nov 9th 2026
3 Weeks
AI-Driven Attribution Testing (Coursera) Coursera
Board Infinity

AI-Driven Attribution Testing (Coursera)

Welcome to AI-Driven Attribution Testing course an engaging and comprehensive course designed to guide you through the fundamental concepts and practical applications of attribution testing powered by artificial intelligence. This course is most suitable for marketers, data analysts, data scientists, and business leaders who aim to leverage data-driven insights for decision-making. It's also beneficial for students and professionals with a keen interest in the convergence of AI, data analysis, and marketing.

Nov 16th 2026
2 Weeks
Convolutional Neural Networks (Coursera) Coursera
DeepLearning.AI

Convolutional Neural Networks (Coursera)

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

Nov 2nd 2026
4 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Nov 9th 2026
4 Weeks
ML Pipelines on Google Cloud (Coursera) Coursera
Google Cloud

ML Pipelines on Google Cloud (Coursera)

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

Nov 9th 2026
4 Weeks
Machine Learning for All (Coursera) Coursera
University of London

Machine Learning for All (Coursera)

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it.

Nov 16th 2026
4 Weeks