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

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
Introduction to Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Introduction to Embedded Machine Learning (Coursera)

Machine learning allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers.

Nov 8th 2026
3 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
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
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
Cadeia de Suprimentos na Nuvem (Coursera) Coursera
FIA Business School

Cadeia de Suprimentos na Nuvem (Coursera)

Nossas boas-vindas ao Curso Cadeia de Suprimentos na Nuvem. Neste curso, você aprenderá como o supply chain pode ampliar o valor da empresa explorando as diversas ferramentas disponíveis em cloud para potencializar a visibilidade e a responsividade da cadeia, melhorando o nível de serviço prestado aos clientes.

Nov 9th 2026
5-12 Weeks
Data Science in Real Life (Coursera) Coursera
Johns Hopkins University

Data Science in Real Life (Coursera)

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

Nov 2nd 2026
1 Week
Preparing for the Google Cloud Professional Data Engineer Exam (Coursera) Coursera
Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam (Coursera)

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Nov 16th 2026
5-12 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
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
Nearest Neighbor Collaborative Filtering (Coursera) Coursera
University of Minnesota

Nearest Neighbor Collaborative Filtering (Coursera)

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.

Nov 2nd 2026
4 Weeks