EdX

Aplicaciones de la Teoría de Grafos a la vida real II (edX)

Aplicaciones de la Teoría de Grafos a la vida real II (edX)

Aprenderemos a modelizar problemas del mundo real mediante su representación con grafos y a resolverlos mediante sus algoritmos asociados. Este curso trata la Teoría de Grafos desde el punto de vista de la modelización, lo que nos permitirá con posterioridad resolver muchos problemas de diversa índole. Presentaremos ejemplos de los distintos problemas en un contexto real, analizaremos la representación de éstos mediante grafos y veremos los algoritmos necesarios para resolverlos.

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

Resolveremos problemas que aparecen en la logística, la robótica, la genética, la sociología, el diseño de redes y el cálculo de rutas óptimas, mediante el uso de la Teoría de Grafos. Nuestro objetivo será presentar tanto los contenidos de la misma como la modelización de los casos planteados.

En cada tema comenzaremos presentando el problema a resolver. Posteriormente introduciremos la teoría y los algoritmos correspondientes, modelizaremos el problema propuesto y finalmente hallaremos su solución. En general explicaremos en qué consiste y cómo se deduce cada algoritmo, haciendo para ello una traza a modo de ejemplo.

Las unidades del curso son:

Unidad 1: Emparejamientos en grafos
Unidad 2: Grafos Eulerianos y Hamiltonianos
Unidad 3: Redes y flujos
Unidad 4: Coloración y localización en mapas

Prerequisites:
Para seguir este curso debes haber completado anteriormente el curso Aplicaciones de la Teoría de Grafos a la vida real (I), también en edx.org

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

Related Courses

Statistical Inference and Modeling for High-throughput Experiments (edX) EdX
HarvardX,Harvard University

Statistical Inference and Modeling for High-throughput Experiments (edX)

A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

Self Paced
Self-Paced
Hacking PostgreSQL: Data Access Methods (edX) EdX
Ural Federal University,UrFUx

Hacking PostgreSQL: Data Access Methods (edX)

Learn the science, engineering practices and hacking techniques of data access – core aspects of information processing in a database. This course is about data storage and data processing technologies with examples from PostgreSQL. It is geared toward database core developers, operation systems developers, system architects, and all those who want to understand databases in more detail.

No sessions available
13-24 Weeks
Feature Engineering for Improving Learning Environments (edX) EdX
University of Texas at Arlington,UTArlingtonX

Feature Engineering for Improving Learning Environments (edX)

Every model used to predict a future outcome depends upon the quality of features used. This course focuses on developing better features to create better models. How can data-intensive research methods be used to create more equitable and effective learning environments? In this course, you will learn how data from digital learning environments and administrative data systems can be used to help better understand relevant learning environments, identify students in need of support, and assess changes made to learning environments.

No sessions available
3 Weeks
Introduction to Java Programming: Fundamental Data Structures and Algorithms (edX) EdX
Universidad Carlos III de Madrid - UC3M,UC3Mx

Introduction to Java Programming: Fundamental Data Structures and Algorithms (edX)

Learn to enhance your code by using fundamental data structures and powerful algorithms in Java. In this introductory course, you will learn programming with Java in an easy and interactive way. You will learn about fundamental data structures, such as lists, stacks, queues and trees, and presents algorithms for inserting, deleting, searching and sorting information on these data structures in an efficient way.

Self Paced
Self-Paced
Basic 3D Modeling using Blender (edX) EdX
IIT Bombay,IITBombayX

Basic 3D Modeling using Blender (edX)

Learn basic 3D modeling skills, including modeling, texturing, and lighting, using free and open source tool: Blender. Animation is an emerging medium of communication, especially in education and entertainment domains. More and more learners are aspiring to make a career in animation. However, the animation training is expensive. One of the reasons is the high cost of proprietary software used for animation.

No sessions available
4 Weeks
Introduction to Genomic Data Science (edX) EdX
University of California, San Diego,UC San DiegoX

Introduction to Genomic Data Science (edX)

Join us on the frontier of bioinformatics and learn how to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome.

No sessions available
4 Weeks
Autonomous Mobile Robots (edX) EdX
ETH Zurich,ETHx

Autonomous Mobile Robots (edX)

Basic concepts and algorithms for locomotion, perception, and intelligent navigation. Robots are rapidly evolving from factory workhorses, which are physically bound to their work-cells, to increasingly complex machines capable of performing challenging tasks in our daily environment. The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments.

Self Paced
Self-Paced