Become one of the most in-demand professionals of today. Specialize with thi complete Professional master’s degree in Artificial Intelligence and Knowledge Engineering"

Developments based on Artificial Intelligence have reached numerous applications within the field of engineering. From the automation of numerous procedures in Industry and companies, to process control itself. This means that engineering professionals need to know and master the operation of these complex techniques.

This essential knowledge also becomes the first step to gain access to the development capacity of this type of technology.

Throughout this program, a real working scenario is offered in order to be able to evaluate the convenience of its application in your own project, assessing its real indications, its way of development and the expectations you may have regarding the results.

Through experience you will learn how to develop the necessary knowledge to advance in this field of work. This program, which necessarily requires experience, is reconciled through distance learning and practical teaching, offering a unique option to give your CV the boost you are looking for...

Join the elite with this highly effective instructional program and open new avenues for your professional advancement"

This Professional master’s degree in Artificial Intelligence and Knowledge Engineering, contains the most complete and up-to-date program on the market. Its most notable features are: 

• The latest technology in online teaching software
• A highly visual teaching system, supported by graphic and schematic contents that are easy to assimilate and understand 
• Practical cases presented by practising experts
• State-of-the-art interactive video systems
• Teaching supported by telepractice 
• Continuous updating and recycling systems
• Autonomous learning: full compatibility with other occupations
• Practical exercises for self-evaluation and learning verification
• Support groups and educational synergies: questions to the expert, debate and knowledge forums
• Communication with the teacher and individual reflection work
• Availability of content from any fixed or portable device with internet connection
• Supplementary documentation databases are permanently available, even after the program

A Professional master’s degree that will enable you to work in all areas of Artificial Intelligence and Knowledge Engineering with the solvency of a high-level professional"

Our teaching staff is made up of professionals from different fields related to this specialty. In this way, the intended objective of instructional updating is achieved. A multidisciplinary faculty of trained and experienced professionals in different environments, who will develop the theoretical knowledge efficiently, but above all, will put at your service the practical knowledge derived from their own experience: one of the differential qualities of this program.

he efficiency of the methodological design of this Professional master’s degree, enhances the student's understanding of the subject. Developed by a multidisciplinary team of e-learning experts, the Method integrates the latest advances in educational technology. In this way, you will be able to study with a range of comfortable and versatile multimedia tools that will give you the operability you need in your training.

The design of this program is based on Problem-Based Learning, an approach that views learning as a highly practical process. To achieve this remotely TECH will use telepractice. with the help of an innovative interactive video system and Learning from an Expert you will be able to acquire the knowledge as if you were facing the scenario about which you are currently learning. A concept that will make it possible to integrate and fix learning in a more realistic and permanent way.

With a methodological design based on teaching techniques proven for their effectiveness, this innovative Professional master’s degree in Artificial Intelligence and Knowledge Engineering will take you through different teaching approaches to allow you to learn in a dynamic and effective way"

Our innovative telepractice concept will give you the opportunity to learn through an immersive experience, which will provide you with a faster integration and a much more realistic view of the contents: “learning from an expert"

The contents of this Professional master’s degree have been developed by different experts in the field, with a clear purpose: to ensure that students acquire each and every one of the skills necessary to become true experts in everything related to Artificial Intelligence.

A complete and well-structured program will take you to the highest standards of quality and success.

A comprehensive teaching program, structured in well-developed teaching units, oriented towards learning that is compatible with your personal and professional life"

Module 1. Programming Fundamentals 

1.1. Introduction to Programming

1.1.1. Basic Computer Structure
1.1.2. Software
1.1.3. Programming Languages
1.1.4. Computer Application Life Cycle

1.2. Algorithm Design

1.2.1. Problem Solving
1.2.2. Descriptive Techniques
1.2.3. Algorithm Elements and Structure

1.3. Program Elements

1.3.1. C++ Origin and Features
1.3.2. Development Environment
1.3.3. Concept of Program
1.3.4. Types of Fundamental Data
1.3.5. Operators
1.3.6. Expressions
1.3.7. Statements
1.3.8. Data Input and Output

1.4. Control Sentences

1.4.1. Statements
1.4.2. Branches
1.4.3. Loops

1.5. Abstraction and Modularity: Function

1.5.1. Modular Design
1.5.2. Concept of Function and Utility
1.5.3. Definition of Function
1.5.4. Execution Flow in a Function Call
1.5.5. Function Prototypes
1.5.6. Results Return
1.5.7. Calling Functions: Parameters
1.5.8. Parameter Passing According to Reference and Value
1.5.9. Scope Identifier

1.6. Statistical Data Structures

1.6.1. Arrays
1.6.2. Matrices Polyhedra
1.6.3. Searching and Sorting
1.6.4. Chaining: I/O Functions for Chains
1.6.5. Structures: Unions
1.6.6. New Types of Data

1.7. Dynamic Data Structures: Pointers

1.7.1. Concept. Definition of Pointer
1.7.2. Pointer Operators and Operations
1.7.3. Pointer Arrays
1.7.4. Pointers and Arrays
1.7.5. Chain Pointers
1.7.6. Pointers to Structures
1.7.7. Multiple Indirectness
1.7.8. Function Pointers
1.7.9. Passing of Functions, Structures, and Arrays as Function Parameters

1.8. Files

1.8.1. Basic Concepts
1.8.2. File Operations
1.8.3. Types of Files
1.8.4. File Organization
1.8.5. Introduction to C++ Files
1.8.6. Managing Files

1.9. Recursion

1.9.1. Definition of Recursion
1.9.2. Types of Recursion
1.9.3. Advantages and Disadvantages
1.9.4. Considerations
1.9.5. Recursive-Iterative Conversion
1.9.6. Recursion Stack

1.10. Testing and Documentation

1.10.1. Program Testing
1.10.2. White Box Testing
1.10.3. Black Box Testing
1.10.4. Testing Tools
1.10.5. Program Documentation

Module 2. Data Structure

2.1. Introduction to C++ Programming

2.1.1. Classes, Constructors, Methods and Attributes
2.1.2. Variables
2.1.3. Conditional Expressions and Loops
2.1.4. Objects

2.2. Abstract Data Types (ADT)

2.2.1. Types of Data
2.2.2. Basic Structures and ADT
2.2.3. Vectors and Arrays

2.3. Lineal Data Structures

2.3.1. ADT Ready Definition
2.3.2. Linked and Double-Linked Lists
2.3.3. Ordered Lists
2.3.4. C++ Lists
2.3.5. ADT Stack
2.3.6. ADT Queue
2.3.7. Stack and Queue in C++

2.4. Hierarchical Data Structures

2.4.1. ADT Tree
2.4.2. Tours
2.4.3. N-ary Trees
2.4.4. Binary Trees
2.4.5. Binary Search Trees

2.5. Hierarchical Data Structures: Complex Trees

2.5.1 Perfectly Balanced Trees or Trees of Minimum Height
2.5.2. Multi-Path Trees
2.5.3. Bibliographical References

2.6. Priority Mounds and Queue

2.6.1. ADT Mounds
2.6.2. ADT Priority Queue

2.7. Hash Tables

2.7.1. ADT Hash Table 
2.7.2. Hash Functions 
2.7.3. Hash Function in Hash Tables
2.7.4. Redispersion
2.7.5. Open Hash Tables

2.8. Graphs

2.8.1. ADT Graph
2.8.2. Types of Graph
2.8.3. Graphical Representation and Basic Operations
2.8.4. Graph Design

2.9. Algorithms and Advanced Graph Concepts

2.9.1. Problems about Graphs
2.9.2. Road Algorithms
2.9.3. Search Algorithms or Paths
2.9.4. Other Algorithms

2.10. Other Data Structure

2.10.1. Sets
2.10.2. Parallel Arrays
2.10.3. Symbol Tables
2.10.4. Tries

Module 3. Algorithm and Complexity

3.1. Introduction to Algorithm Design Strategies

3.1.1 Recursion
3.1.2 Divide and Conquer
3.1.3 Other Strategies 

3.2. Algorithm Efficiency and Analysis

3.2.1. Efficiency Measures
3.2.2. Measuring Entry Size
3.2.3. Measuring Execution Time
3.2.4. Worst, Best and Average Case
3.2.5. Asymptotic Notation
3.2.6. Mathematical Analysis Criteria for Non-Recursive Algorithms
3.2.7. Mathematical Analysis for Recursive Algorithms
3.2.8. Empirical Analysis for Algorithms

3.3. Sorting Algorithms

3.3.1. Concept of Sorting
3.3.2. Bubble Sorting
3.3.3. Selection Sorting
3.3.4. Insertion Sorting
3.3.5. Merge Sort
3.3.6. (Quick Sort)

3.4 Tree-Based Algorithms

3.4.1. Concept of Tree
3.4.2. Binary Trees
3.4.3. Tree Range
3.4.4. Representing Expressions
3.4.5. Sorted Binary Trees
3.4.6. Balanced Binary Trees

3.5. Algorithms Using Heaps

3.5.1. Heaps
3.5.2. The Heapsort Algorithm
3.5.3. Priority Queues

3.6. Graph Algorithms

3.6.1. Representation
3.6.2. Width Range
3.6.3. In-Depth Range
3.6.4. Topological Sorting

3.7. Greedy Algorithms

3.7.1. Greedy Strategy
3.7.2. Greedy Strategy Elements
3.7.3. Currency Exchange
3.7.4. Traveler's Problem
3.7.5. The Backpack Problem

3.8. Search for Minimum Paths

3.8.1. Problem of the Minimum Path
3.8.2. Negative Arcs and Cycles
3.8.3. Dijkstra's Algorithm

3.9. Greedy Algorithms on Graphs

3.9.1. The Minimum Overlapping Tree
3.9.2. Prim's Algorithm
3.9.3. Kruskal’s Algorithm
3.9.4. Complexity Analysis

3.10. Backtracking

3.10.1. Backtracking
3.10.2. Alternative Techniques

Module 4. Advanced  Algorithms Design

4.1. Analysis of Recursive and Divide and Conquer Algorithms

4.1.1. Posing and Solving Homogeneous and Non-Homogeneous Recurrence Equations
4.1.2. Divide and Conquer Strategy Overview

4. 2. Amortized Analysis

4.2.1. Aggregate Analysis
4.2.2. The Accounting Method
4.2.3. The Potential Method

4.3. Dynamic Programming and Algorithms for NP Problems

4.3.1. Dynamic Programming Features
4.3.2. Backtracking
4.3.3. Branching and Pruning

4.4. Combinatorial Optimization

4.4.1. Representation of Problems
4.4.2. Optimization in 1D

4.5. Randomization Algorithms

4.5.1. Examples of Randomization Algorithms
4.5.2. The Buffon Theorem
4.5.3. Monte Carlo Algorithm
4.5.4. Las Vegas Algorithm

4.6. Local and Candidate Search

4.6.1. Garcient Ascent
4.6.2. Hill Climbing
4.6.3. Simulated Annealing
4.6.4. Tabu Search
4.6.5. Candidate Search

4.7. Formal Program Verification

4.7.1. Specification of Functional Abstractions
4.7.2. The Language of First-Order Logic
4.7.3. Hoare's Formal System

4.8. Iterative Program Verification

4.8.1. Rules of Hoare's Formal System
4.8.2. Concept of Invariant Iterations

4.9. Numerical Methods

4.9.1. The Bisection Method
4.9.2. The Newton Raphson Method
4.9.3. The Secant Method

4.10. Parallel Algorithms

4.10.1. Parallel Binary Operations
4.10.2. Parallel Operations with Graphs
4.10.3. Parallelism in Divide and Conquer
4.10.4. Parallelism in Dynamic Programming

Module 5. Computational Logic

5.1. Justification of the Logic

5.1.1. Object of Logic Study
5.1.2. What Is Logic for?
5.1.3. Components and Types of Reasoning
5.1.4. Components of a Logic Calculation
5.1.5. Semantics
5.1.6. Justification of the Existence of a Logic
5.1.7. How to Check that a Logic is Appropriate?

5.2. Calculation of Natural Deduction from Statements

5.2.1. Formal Language
5.2.2. Deductive Mechanism

5.3. Formalization and Deduction Strategies for Propositional Logic

5.3.1. Formalization Strategies
5.3.2. Natural Reasoning
5.3.3. Laws and Rules
5.3.4. Axiomatic Deduction and Natural Deduction
5.3.5. Calculation of the Natural Deduction
5.3.6. Primitive Rules of Propositional Calculus

5.4. Semantics of Propositional Logic

5.4.1. Truth Tables
5.4.2. Equivalence
5.4.3. Tautologies and Contradictions
5.4.4. Validation of Propositional Sentences
5.4.5. Validation by Means of Truth Tables
5.4.6. Validation Using Semantic Trees
5.4.7. Validation by Refutation

5.5. Applications of Propositional Logic: Logic Circuits

5.5.1. Basic Gates
5.5.2. Circuits
5.5.3. Mathematical Models of the Circuits
5.5.4. Minimization
5.5.5. The Second Canonical Form and the Minimum Form in Product of Additions
5.5.6. Other Gates

5.6. Calculation of Natural Deduction of Predicates

5.6.1. Formal Language
5.6.2. Deductive Mechanism

5.7. Formalization Strategies for Predicate Logic

5.7.1. Introduction to Formalization in Predicate Logic
5.7.2. Formalization Strategies with Quantifiers

5.8. Deduction Strategies for Predicate Logic

5.8.1. Reason for Omission
5.8.2. Presentation of the New Rules
5.8.3. Predicate Logic as a Natural Deduction Calculus

5.9. Applications of Predicate Logic: Introduction to Logic Programming

5.9.1. Informal Presentation
5.9.2. Prolog Elements
5.9.3. Re-Evaluation and Cut-Off

5.10. Set Theory, Predicate Logic and Its Semantics

5.10.1. Intuitive Set Theory
5.10.2. Introduction to Predicate Semantics

Module 6. Artificial Intelligence and Knowledge Engineering

6.1. Introduction to Artificial Intelligence and Knowledge Engineering

6.1.1. Brief History of Artificial Intelligence
6.1.2. Artificial Intelligence Today
6.1.3. Knowledge Engineering

6.2. Searching

6.2.1. Common Search Concepts
6.2.2. Uninformed Search
6.2.3. Informed Search

6.3. Boolean Satisfiability, Constraint Satisfiability and Automatic Planning

6.3.1. Boolean Satisfiability
6.3.2. Constraint Satisfaction Problems
6.3.3. Automatic Planning and PDDL
6.3.4. Planning as a Heuristic Search
6.3.5. Planning with SAT

6.4. Artificial Intelligence in Games

6.4.1. Game Theory
6.4.2. Minimax and Alpha-Beta Pruning
6.4.3. Simulation: Monte Carlo

6.5. Supervised and Unsupervised Learning

6.5.1. Introduction to Machine Learning
6.5.2. Classification
6.5.3. Regression
6.5.4. Validation of Results
6.5.5. Clustering

6.6. Neural Networks

6.6.1. Biological Fundamentals
6.6.2. Computational Model
6.6.3. Supervised and Unsupervised Neural Networks
6.6.4. Simple Perceptron
6.6.5. Multilayer Perceptron

6.7. Genetic Algorithms

6.7.1. History
6.7.2. Biological Base
6.7.3. Problem Coding 
6.7.4. Generation of the Initial Population
6.7.5. Main Algorithm and Genetic Operators
6.7.6. Evaluation of Individuals: Fitness

6.8. Thesauri, Vocabularies, Taxonomies

6.8.1. Vocabulary
6.8.2. Taxonomy
6.8.3. Thesauri
6.8.4. Ontologies

6.9. Knowledge Representation: Semantic Web

6.9.1. Semantic Web
6.9.2. Specifications: RDF, RDFS and OWL
6.9.3. Inference/Reasoning
6.9.4. Linked Data

6.10. Expert Systems and DSS

6.10.1. Expert Systems
6.10.2. Decision Support Systems

Module 7. Intelligent Systems

7.1. Agent Theory

7.1.1. Concept History
7.1.2. Agent Definition
7.1.3. Agents in Artificial Intelligence
7.1.4. Agents in Software Engineering

7.2. Agent Architectures

7.2.1. The Reasoning Process of an Agent
7.2.2. Reactive Agents
7.2.3. Deductive Agents
7.2.4. Hybrid Agents
7.2.5. Comparison

7.3. Information and Knowledge

7.3.1. Difference between Data, Information and Knowledge
7.3.2. Data Quality Assessment
7.3.3. Data Collection Methods
7.3.4. Information Acquisition Methods
7.3.5. Knowledge Acquisition Methods

7.4. Knowledge Representation

7.4.1. The Importance of Knowledge Representation
7.4.2. Definition of Knowledge Representation According to Roles
7.4.3. Knowledge Representation Features

7.5. Ontologies

7.5.1. Introduction to Metadata
7.5.2. Philosophical Concept of Ontology
7.5.3. Computing Concept of Ontology
7.5.4. Domain Ontologies and Higher-Level Ontologies
7.5.5. How to Build an Ontology?

7.6. Ontology Languages and Ontology Creation Software

7.6.1. Triple RDF, Turtle and N3
7.6.2. RDF Schema
7.6.3. OWL
7.6.4. SPARQL
7.6.5. Introduction to Ontology Creation Tools
7.6.6. Installing and Using Protégé

7.7. Semantic Web

7.7.1. Current and Future Status of the Semantic Web
7.7.2. Semantic Web Applications

7.8. Other Knowledge Representation Models

7.8.1. Vocabulary
7.8.2. Global Vision
7.8.3. Taxonomy
7.8.4. Thesauri
7.8.5. Folksonomy
7.8.6 Comparison
7.8.7. Mind Maps

7.9. Knowledge Representation Assessment and Integration

7.9.1. Zero-Order Logic
7.9.2. First-Order Logic
7.9.3. Descriptive Logic
7.9.4. Relationship between Different Types of Logic
7.9.5. Prolog: Programming Based on First-Order Logic

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

7.10.1. Concept of Reasoner
7.10.2. Reasoner Applications
7.10.3. Knowledge-Based Systems
7.10.4. MYCIN: History of Expert Systems
7.10.5. Expert Systems Elements and Architecture
7.10.6. Creating Expert Systems

Module 8. Machine Learning and Data Mining

8.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

8.1.1. Key Concepts of Knowledge Discovery Processes
8.1.2. Historical Perspective of Knowledge Discovery Processes
8.1.3. Stages of the Knowledge Discovery Processes
8.1.4. Techniques Used in Knowledge Discovery Processes
8.1.5. Characteristics of Good Machine Learning Models
8.1.6. Types of Machine Learning Information
8.1.7. Basic Learning Concepts
8.1.8. Basic Concepts of Unsupervised Learning

8.2. Data Exploration and Pre-processing

8.2.1. Data Processing
8.2.2. Data Processing in the Data Analysis Flow
8.2.3. Types of Data
8.2.4. Data Transformations
8.2.5. Visualization and Exploration of Continuous Variables
8.2.6. Visualization and Exploration of Categorical Variables
8.2.7. Correlation Measures
8.2.8. Most Common Graphic Representations
8.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

8.3. Decision Trees

8.3.1. ID3 Algorithm
8.3.2. C4.5 Algorithm
8.3.3. Overtraining and Pruning
8.3.4. Analysis of Results

8.4. Evaluation of Classifiers

8.4.1. Confusion Matrixes
8.4.2. Numerical Evaluation Matrixes
8.4.3. Kappa Statistic
8.4.5. The Roc Curve

8.5. Classification Rules

8.5.1. Rule Evaluation Measures
8.5.2. Introduction to Graphic Representation
8.5.3. Sequential Overlay Algorithm

8.6. Neural Networks

8.6.1. Basic Concepts
8.6.2. Simple Neural Networks
8.6.3. Backpropagation Algorithm
8.6.4. Introduction to Recurrent Neural Networks

8.7. Bayesian Methods

8.7.1. Basic Probability Concepts
8.7.2. Bayes' Theorem
8.7.3. Naive Bayes
8.7.4. Introduction to Bayesian Networks

8.8. Regression and Continuous Response Models

8.8.1. Simple Linear Regression
8.8.2. Multiple Linear Regression
8.8.3. Logistic Regression
8.8.4. Regression Trees
8.8.5. Introduction to Support Vector Machines (SVM)
8.8.6. Goodness-of-Fit Measures

8.9. Clustering

8.9.1. Basic Concepts
8.9.2. Hierarchical Clustering
8.9.3. Probabilistic Methods
8.9.4. EM Algorithm
8.9.5. B-Cubed Method
8.9.6. Implicit Methods

8.10. Text Mining and Natural Language Processing (NLP)

8.10.1. Basic Concepts
8.10.2. Corpus Creation
8.10.3. Descriptive Analysis
8.10.4. Introduction to Feelings Analysis

Module 9. Multiagent Systems and Computational Perception

9.1. Agents and Multiagent Systems

9.1.1. Concept of Agent
9.1.2. Architecture
9.1.3. Communication and Coordination
9.1.4. Programming Languages and Tools
9.1.5. Applications of the Agents
9.1.6. The FIPA

9.2. The Standard for Agents FIPA

9.2.1. Communication between Agents
9.2.2. Agent Management
9.2.3. Abstract Architecture
9.2.4. Other Specifications

9.3. The JADE Platform

9.3.1. Software Agents According to JADE
9.3.2.Architecture
9.3.3. Installation and Execution
9.3.4. JADE Packages

9.4. Basic Programming with JADE

9.4.1. The Management Console
9.4.2. Basic Creation of Agents

9.5. Advanced Programming with JADE

9.5.1. Advanced Creation of Agents
9.5.2. Communication between Agents
9.5.3. Discovering Agents

9.6. Artificial Vision.

9.6.1. Processing and Digital Analysis of Images
9.6.2. Image Analysis and Artificial Vision
9.6.3. Image Processing and Human Vision
9.6.4. Image Capturing System
9.6.5. Image Formation and Perception

9.7. Digital Image Analysis

9.7.1. Stages of the Image Analysis Process
9.7.2. Pre-Processing
9.7.3. Basic Operations
9.7.4. Spatial Filtering

9.8. Digital Image Transformation and Image Segmentation

9.8.1. Fourier Transform
9.8.2. Frequency Filtering
9.8.3. Basic Concepts
9.8.4. Thresholding
9.8.5. Contour Detection

9.9. Shape Recognition

9.9.1. Feature Extraction
9.9.2. Classification Algorithms

9.10. Natural Language Processing

9.10.1. Automatic Speech Recognition
9.10.2. Computational Linguistics

Module 10. Bio-Inspired Computing

10.1. Introduction to Bio-Inspired Computing

10.1.1. Introduction to Bio-Inspired Computing

10.2. Social Adaptation Algorithms

10.2.1. Bio-Inspired Computing Based on Ant Colonies
10.2.2. Variants of Ant Colony Algorithms
10.2.3. Particle Cloud Computing

10.3. Genetic Algorithms

10.3.1. General Structure
10.3.2. Implementations of the Main Operators

10.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

10.4.1. CHC Algorithm
10.4.2. Multimodal Problems

10.5. Models of Evolutionary Computing (I)

10.5.1. Evolutionary Strategies
10.5.2. Evolutionary Programming
10.5.3. Algorithms Based on Differential Evolution

10.6. Models of Evolutionary Computing (II)

10.6.1. Evolution Models Based on Estimation of Distributions (EDA)
10.6.2. Genetic Programming

10.7. Developmental Programming Applied to Learning Disabilities

10.7.1. Rules-Based Learning
10.7.2. Evolutionary Methods in Instance Selection Problems

10.8. Multi-Objective Problems

10.8.1. Concept of Dominance
10.8.2. Application of Evolutionary Algorithms to Multi-objective Problems

10.9. Neural Networks (I)

10.9.1. Introduction to Neural Networks
10.9.2. Practical Example with Neural Networks

10.10. Neural Networks (II)

10.10.1. Use Cases of Neural Networks in Medical Research
10.10.2. Use Cases of Neural Networks in Economy
10.10.3. Use Cases of Neural Networks in Artificial Vision

A unique specialization program that will allow you to acquire advanced training in this field"