This Postgraduate diploma will allow you up to date your knowledge in Human Intelligent Systems in a practical way, 100% online, without renouncing to the maximum educational rigor”

This program is aimed at those interested in attaining a higher level of knowledge Intelligence Systems. The main objective of this Postgraduate diploma is for students to specialize their knowledge in simulated work environments and conditions in a rigorous and realistic manner so that they can later apply it in the real world.

This program will prepare scientifically and technologically, as well as to develop the professional practice of software engineering, with a transversal and versatile approach adapted to the new technologies and innovations in this field. Students will gain extensive knowledge Intelligence Systems from professionals in the field.

The professional must Seize the opportunity to take this educational program in a 100% online format, without having to give up obligations, which will make it easier to continue studying. Up to date your knowledge and get a Postgraduate diploma to continue growing personally and professionally.

This program will enhance your skills and update your knowledge of Intelligent Systems”

This Postgraduate diploma in Intelligent Systems contains the most complete and up-to-date program on the market. The most important features include:

• Development of 100 simulated scenarios presented by experts in Information Systems
• Its graphic, schematic and practical contents, with which they are conceived, gather scientific and practical information on Information Systems
• News on the latest developments in Information Systems
• It contains practical exercises where the self-assessment process can be carried out to improve learning
• Interactive learning system based on the case method and its application to real practice
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Learn about Intelligent Systems with this intensive program from the comfort of your home”

It includes in its teaching staff professionals belonging to the field of education, who bring to this program their work experience, in addition to recognized specialists belonging to reference societies and prestigious universities.

Thanks to its multimedia content developed with the latest educational technology, they will allow the professional a situated and contextual learning, that is to say, a simulated environment that will provide an immersive learning programmed to prepare in real situations.

The program design is based on Problem-Based Learning,  through which teachers must try to solve the different professional practice situations that arise throughout the course. To do so, the professional will be assisted by an innovative, interactive video system created by renowned experts in Intelligent Systems with extensive teaching experience.

Take advantage of the latest educational technology to up to date on Intelligent Systems from the comfort of your home"

Learn about the latest techniques in Intelligent Systems from experts in the field"

The structure of the contents has been designed by a team of IT engineering professionals, aware of the relevance of current preparation in order to delve into this area of knowledge , in order to humanistically enrich the students and raise the level of knowledge in Intelligent Systems through the latest educational technologies available.

This Postgraduate diploma in Intelligent Systems contains the most complete and up-to-date program of learning the market”

Module 1. Intelligent Systems

1.1. Agent Theory

1.1.1. Concept History
1.1.2. Agent Definition
1.1.3. Agents in Artificial Intelligence
1.1.4. Agents in Software Engineering

1.2. Agent Architectures

1.2.1. The Reasoning Process of an Agent
1.2.2. Reactive Agents
1.2.3. Deductive Agents
1.2.4. Hybrid Agents
1.2.5. Comparison

1.3. Information and Knowledge

1.3.1. Difference between Data, Information and Knowledge
1.3.2. Data Quality Assessment
1.3.3. Data Collection Methods
1.3.4. Information Acquisition Methods
1.3.5. Knowledge Acquisition Methods

1.4. Knowledge Representation

1.4.1. The Importance of Knowledge Representation
1.4.2. Definition of Knowledge Representation According to Roles
1.4.3. Knowledge Representation Features

1.5. Ontologies

1.5.1. Introduction to Metadata
1.5.2. Philosophical Concept of Ontology
1.5.3. Computing Concept of Ontology
1.5.4. Domain Ontologies and Higher-Level Ontologies
1.5.5. Building an Ontology

1.6.  Ontology Languages and Ontology Creation Software

1.6.1. Triple RDF, Turtle and N3
1.6.2. RDF Schema
1.6.3. OWL
1.6.4. SPARQL
1.6.5. Introduction to Ontology Creation Tools
1.6.6. Installing and Using Protégé

1.7.  Semantic Web

1.7.1. Current and Future Status of the Semantic Web
1.7.2. Semantic Web Applications

1.8.  Other Knowledge Representation Models

1.8.1. Vocabulary
1.8.2. Global Vision
1.8.3. Taxonomy
1.8.4. Thesauri
1.8.5. Folksonomy
1.8.6. Comparison
1.8.7. Mind Maps

1.9. Knowledge Representation Assessment and Integration

1.9.1. Zero-Order Logic
1.9.2. First-Order Logic
1.9.3. Descriptive Logic
1.9.4. Relationship between Different Types of Logic
1.9.5. Prolog: Programming Based on First-Order Logic

1.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

1.10.1. Concept of Reasoner
1.10.2. Reasoner Applications
1.10.3. Knowledge-Based Systems
1.10.4. MYCIN: History of Expert Systems
1.10.5. Expert Systems Elements and Architecture
1.10.6. Creating Expert Systems

Module 2. Artificial Intelligence and Knowledge Engineering

2.1. Introduction to Artificial Intelligence and Knowledge Engineering

2.1.1. Brief History of Artificial Intelligence
2.1.2. Artificial Intelligence Today
2.1.3. Knowledge Engineering

2.2. Searching

2.2.1. Common Search Concepts
2.2.2. Uninformed Search
2.2.3. Informed Search

2.3. Boolean Satisfiability, Constraint Satisfiability and Automatic Planning

2.3.1. Boolean Satisfiability
2.3.2. Constraint Satisfiability Problems
2.3.3. Automatic Planning and PDDL
2.3.4. Planning as a Heuristic Search
2.3.5. Planning with SAT

2.4. Artificial Intelligence in Games

2.4.1. Game Theory
2.4.2. Minimax and Alpha-Beta Pruning
2.4.3. Simulation: Monte Carlo

2.5. Supervised and Unsupervised Learning

2.5.1. Introduction to Machine Learning
2.5.2. Classification
2.5.3. Regression
2.5.4. Validation of Results
2.5.5. Clustering

2.6. Neural Networks

2.6.1. Biological Fundamentals
2.6.2. Computational Model
2.6.3. Supervised and Unsupervised Neural Networks
2.6.4. Simple Perceptron
2.6.5. Multilayer Perceptron.

2.7. Genetic Algorithms

2.7.1. History
2.7.2. Biological Basis
2.7.3. Problem Coding
2.7.4. Generation of the Initial Population
2.7.5. Main Algorithm and Genetic Operators
2.7.6. Evaluation of Individuals: Fitness

2.8. Thesauri, Vocabularies, Taxonomies

2.8.1. Vocabulary
2.8.2. Taxonomy
2.8.3. Thesauri
2.8.4. Ontologies

2.9. Knowledge Representation: Semantic Web

2.9.1. Semantic Web
2.9.2. Specifications RDF, RDFS and OWL
2.9.3. Inference/ Reasoning
2.9.4. Linked Data

2.10. Expert systems and DSS

2.10.1. Expert Systems
2.10.2. Decision Support Systems

Module 3. Multiagent Systems and Computational Perception

3.1. Agents and Multiagent Systems

3.1.1. Concept of Agent
3.1.2. Architecture
3.1.3. Communication and Coordination
3.1.4. Programming Languages and Tools
3.1.5. Applications of the Agents
3.1.6. The FIPA

3.2. The Standard for Agents: FIPA

3.2.1. Communication between Agents
3.2.2. Agent Management
3.2.3. Abstract Architecture
3.2.4. Other Specifications

3.3. The JADE Platform

3.3.1. Software Agents According to JADE
3.3.2. Architecture
3.3.3. Installation and Execution
3.3.4. JADE Packages

3.4. Basic Programming with JADE

3.4.1. The Management Console
3.4.2. Basic Creation of Agents

3.5. Advanced Programming with JADE

3.5.1. Advanced Creation of Agents
3.5.2. Communication between Agents
3.5.3. Discovering Agents

3.6. Computer Vision

3.6.1. Processing and Digital Analysis of Images
3.6.2. Image Analysis and Artificial Vision
3.6.3. Image Processing and Human Vision
3.6.4. Image Capturing System
3.6.5. Image Formation and Perception

3.7. Digital Image Analysis

3.7.1. Stages of the Image Analysis Process
3.7.2. Pre-Processing
3.7.3. Basic Operations
3.7.4. Spatial Filtering

3.8. Digital Image Transformation and Image Segmentation

3.8.1. Fourier Transform
3.8.2. Frequency Filtering
3.8.3. Basic Concepts
3.8.4. Thresholding
3.8.5. Contour Detection

3.9. Shape Recognition

3.9.1. Feature Extraction
3.9.2. Classification Algorithms

3.10. Natural Language Processing

3.10.1. Automatic Speech Recognition
3.10.2. Computational Linguistics

A unique, key, and decisive educational experience to boost your professional development”