With a well cared and worked Personal Branding, you will be able to reach the most privileged positions"

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class centre for intensive managerial skills training.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that combines the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system.  

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future. 

100,000+ executives trained each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents. 

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (a postgraduate learning methodology with the highest international rating) with the Case Study. A complex balance between tradition and state-of-the-art, within the context of the most demanding academic itinerary.  

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.  

At TECH, you will have access to the most rigorous and up-to-date case studies in the academic community”

This TECH Global University program has been designed to meet the specialization needs of business professionals who wish to expand their knowledge of computer security, a fundamental field in order to be able to control potential threats that can pose a great risk to the company. Therefore, this Professional Master's Degree will allow them to acquire specific knowledge that they can apply to their work practice. And, to do so, they will use a totally online methodology so they can balance their studies with the rest of their daily obligations. 

This program will be essential to detect possible cyber-attacks in your company" 

Syllabus

The Professional Master's Degree in Artificial Intelligence and Knowledge Engineering at TECH Global University is an intensive program that prepares students to face challenges and business decisions in the field of information security. Its content is designed to promote the development of managerial skills that enable more rigorous decision-making in uncertain environments. 

Throughout 1,500 hours of study, students will face a multitude of practical cases through individual work, which will allow the student to acquire the necessary skills to successfully carry out their daily practice. It is, therefore, an authentic immersion in real business situations. 

This program deals in depth with different areas of the company and is designed for managers to understand Artificial Intelligence from a strategic, international and innovative perspective. 

A program designed especially for students, focused on their professional development, which prepares them to achieve excellence in the field of information security management and administration. A program that understands your needs and those of your company through innovative content based on the latest trends, and supported by the best educational methodology and an exceptional faculty, which will provide you with the competencies to solve critical situations in a creative and efficient way.

This Professional Master's Degree takes place over 12 months and is divided into 10 modules:

Module 1. Programming Fundamentals
Module 2. Data Structure
Module 3. Algorithms and Complexity
Module 4. Advanced Algorithm Design
Module 5. Logic in Computer Science 
Module 6. Artificial Intelligence and Knowledge Engineering
Module 7. Intelligent Systems
Module 8. Machine Learning and Data Mining 
Module 9. Multiagent Systems and Computational Perception 
Module 10. Bio-Inspired Computing

Where, when and how is it taught?

TECH offers the possibility of developing this Executive Master’s Degree in Artificial Intelligence and Knowledge Engineering completely online. Over the course of 12 months, you will be able to access all the contents of this program at any time, allowing you to self-manage your study time.

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 List: 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 Heaps 
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 Graphs 
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. Algorithms 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 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. 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 Algorithm 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. Logic in Computer Science 

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 Adequate 

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. Calculating 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. Natural Predicate Deduction Calculus 

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 Basis 
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. Building 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 
8.4.4. ROC Curves 

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. JADEPackages 

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 Computer Vision 

This program will open doors to a new professional world”