With this innovative 100% online university program, you will master the most advanced techniques of modern cryptography, and design robust protection systems to ensure the privacy and authenticity of data”

Artificial Intelligence and Cybersecurity are two fundamental pillars in the digital era. While the former focuses on the development of systems capable of simulating human cognitive processes, Cybersecurity is responsible for protecting computer systems and data from malicious attacks. The combination of both disciplines makes it possible to create advanced solutions that not only detect and mitigate threats in real time, but also anticipate potential vulnerabilities, thus ensuring a safer digital environment. This context drives the need for highly qualified professionals who master both the fundamentals of Artificial Intelligence and its specific applications in cyber defense.

From these demands arises the Master's Degree in Artificial Intelligence in Cybersecurity of TECH, a program structured in 20 comprehensive modules that address from the fundamentals of Artificial Intelligence and data management to deep learning, convolutional neural networks and the application of generative models in Cybersecurity. It also delves into threat detection, digital forensics and modern cryptography, using tools such as TensorFlow and advanced Artificial Intelligence models to meet the challenges of a constantly evolving digital environment. Therefore, this academic path enables computer scientists to anticipate emerging threats and lead security strategies in complex environments.

Regarding the methodology of this university program, TECH offers a 100% online environment that allows professionals to individually plan their schedules and pace of study. In addition, it uses its disruptive Relearning system, which facilitates the progressive assimilation of key concepts through contextualized reiteration and active learning. Along the same lines, graduates will only need an electronic device with an Internet connection to access the Virtual Campus. There they will be able to access a vast library of multimedia resources, such as interactive summaries, explanatory videos or specialized readings based on the latest evidence.

You will delve into how Artificial Intelligence transforms Cybersecurity with tools such as Neural Networks and generative models applied to threat detection and prevention”

This Master's Degree in Artificial Intelligence in Cybersecurity contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Artificial Intelligence, Cybersecurity and advanced technologies
• The graphic, schematic and eminently practical contents with which it is conceived gather scientific and practical information on those disciplines that are indispensable for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• 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

You will optimize your strategic decision making through predictive analytics and the use of advanced models in cyber attack management”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this specializing program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts.

You will have at your disposal the most cutting-edge multimedia resources, from interactive overviews to explanatory videos and specialized readings"

You'll lead projects in key sectors, such as infrastructure protection and management of connected Internet of Things systems"

The syllabus of this Master's Degree addresses both the fundamentals of Artificial Intelligence and its specific applications in the field of Cybersecurity. Throughout this academic course, computer scientists will delve into key topics such as algorithms, data mining and natural language processing. They will also delve into advanced neural networks and intelligent systems applied to forensic analysis, as well as intrusion detection and proactive defense, which will allow them to acquire the necessary tools to develop innovative solutions to digital threats.

With the Relearning methodology, of which TECH is a pioneer, you will specialize in the use of Bio-inspired Systems and Deep Learning to address complex problems in digital protection”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Film
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation: Semantic Web

1.6. Semantic Web

1.6.1. Specifications: RDF, RDFS and OWL
1.6.2. Inference/ Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, Whatsapp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to Its Shape

2.2.2.1. Numeric
2.2.2.2. Text
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
2.7.3. Data Mining

2.8. Datawarehouse

2.8.1. Elements that Comprise It
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory Aspects

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model
3.10.3. Useful Tools

Module 4. Data Mining: Selection, Pre-Processing and Transformation

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data
4.3.3. Transforming Attributes

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Pre-Processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Mathematical Analysis Criteria for Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Sorting by Merge (Merge_Sort)
5.3.6. Sorting Quickly (Quick_Sort)

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology

6.6. Ontology Languages and Ontology Creation Software

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

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

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

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

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Pre-Processing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. Algorithm C
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrices
7.4.2. Numerical Evaluation Matrices
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10. Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Operations

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Hidden Layer
8.3.3. Output Layer

8.4. Union of Layers and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the First Neural Network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Transfer Learning Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Adam and RMSprop Optimizers
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Transfer Learning Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Transfer Learning Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Graph Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Pre-Processing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction with Keras
10.8.3. Using the Keras Pre-Processing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Data Pre-Processing with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Application
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Using the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Cortex Visual Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet- Using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-Trained Keras Models

11.6.1. Characteristics of Pre-Trained Models
11.6.2. Uses of Pre-Trained Models
11.6.3. Advantages of Pre-Trained Models

11.7. Pre-Trained Models for Transfer Learning

11.7.1. Transfer Learning
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.1. Edge Detection
11.10.1. Rule-Based Segmentation Methods

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

12.1. Text Generation Using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Using Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Pre-Processing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Application

12.10.1. Development of a Natural Language Processing Application with RNN and Attention.
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Application
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence in Financial Services. Opportunities and Challenges
15.1.2. Case Studies
15.1.3. Potential Risks Related to the Use of Artificial Intelligence
15.1.4. Potential Future Developments / Uses of Artificial Intelligence

15.2. Implications of Artificial Intelligence in Healthcare Service

15.2.1. Implications of Artificial Intelligence in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Studies

15.3. Risks Related to the Use of Artificial Intelligence in Health Services

15.3.1. Potential Risks Related to the Use of Artificial Intelligence
15.3.2. Potential Future Developments / Uses of Artificial Intelligence

15.4. Retail

15.4.1. Implications of Artificial Intelligence in Retail. Opportunities and Challenges
15.4.2. Case Studies
15.4.3. Potential Risks Related to the Use of Artificial Intelligence
15.4.4. Potential Future Developments / Uses of Artificial Intelligence

15.5. Industry

15.5.1. Implications of Artificial Intelligence in Industry. Opportunities and Challenges
15.5.2. Case Studies

15.6. Potential Risks Related to the Use of Artificial Intelligence in the Industry

15.6.1. Case Studies
15.6.2. Potential Risks Related to the Use of Artificial Intelligence
15.6.3. Potential Future Developments / Uses of Artificial Intelligence

15.7. Public Administration

15.7.1. Implications of Artificial Intelligence in Public Administration. Opportunities and Challenges
15.7.2. Case Studies
15.7.3. Potential Risks Related to the Use of Artificial Intelligence
15.7.4. Potential Future Developments / Uses of Artificial Intelligence

15.8. Educational

15.8.1. Implications of Artificial Intelligence in Education. Opportunities and Challenges
15.8.2. Case Studies
15.8.3. Potential Risks Related to the Use of Artificial Intelligence
15.8.4. Potential Future Developments / Uses of Artificial Intelligence

15.9. Forestry and Agriculture

15.9.1. Implications of Artificial Intelligence in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Studies
15.9.3. Potential Risks Related to the Use of Artificial Intelligence
15.9.4. Potential Future Developments / Uses of Artificial Intelligence

15.10. Human Resources

15.10.1. Implications of Artificial Intelligence in Human Resources. Opportunities and Challenges
15.10.2. Case Studies
15.10.3. Potential Risks Related to the Use of Artificial Intelligence
15.10.4. Potential Future Developments / Uses of Artificial Intelligence

Module 16. Cybersecurity and Modern Threat Analysis with ChatGPT

16.1. Introduction to Cybersecurity: Current Threats and the Role of Artificial Intelligence

16.1.1. Definition and Basic Concepts of Cybersecurity
16.1.2. Types of Modern Cybersecurity Threats
16.1.3. Role of Artificial Intelligence in the Evolution of Cybersecurity

16.2. Confidentiality, Integrity and Availability (CIA) in the Age of Artificial Intelligence

16.2.1. Fundamentals of the CIA Model in Cybersecurity
16.2.2. Security Principles Applied in the Artificial Intelligence Context
16.2.3. CIA Challenges and Considerations in Artificial Intelligence-Driven Systems

16.3. Use of ChatGPT for Risk Analysis and Threat Scenarios

16.3.1. Fundamentals of Risk Analysis in Cybersecurity
16.3.2. ChatGPT's Ability to Identify and Evaluate Threat Scenarios
16.3.3. Benefits and Limitations of Risk Analysis with Artificial Intelligence

16.4. ChatGPT in the Detection of Critical Vulnerabilities

16.4.1. Principles of Vulnerability Detection in Information Systems
16.4.2. ChatGPT Functionalities to Support Vulnerability Detection
16.4.3. Ethical and Security Considerations When Using Artificial Intelligence in Fault Detection

16.5. AI-Assisted Analysis of Malware and Ransomware

16.5.1. Basic Principles of Malware and Ransomware Analysis
16.5.2. Artificial Intelligence Techniques Applied in the Identification of Malicious Code
16.5.3. Technical and Operational Challenges in AI-Assisted Malware Analysis

16.6. Identification of Common Attacks with Artificial Intelligence:  Phishing, Social Engineering and Exploitation

16.6.1. Classification of Attacks: Phishing, Social Engineering and Exploitation
16.6.2. Artificial Intelligence Techniques for Identification and Analysis of Common Attacks
16.6.3. Difficulties and Limitations of Artificial Intelligence Models for Attack Detection

16.7. ChatGPT in Cyberthreat Training and Simulation

16.7.1. Fundamentals of Threat Simulation for Cybersecurity Training 
16.7.2. ChatGPT Capabilities for Designing Simulation Scenarios
16.7.3. Benefits of Threat Simulation as a Training Tool

16.8. Cyber Security Policies with Artificial Intelligence Recommendations

16.8.1. Principles for Cyber Security Policy Formulation
16.8.2. Role of Artificial Intelligence in Generating Security Recommendations
16.8.3. Key Components in Artificial Intelligence Oriented Security Policies

16.9. Security in IoT Devices and the Role of Artificial Intelligence

16.9.1. Fundamentals of Internet of Things (IoT) Security
16.9.2. Artificial Intelligence Capabilities to Mitigate Vulnerabilities in IoT Devices
16.9.3. Specific Artificial Intelligence Challenges and Considerations for IoT Security

16.10. Threat Assessment and Responses Assisted by Artificial Intelligence Tools

16.10.1. Cybersecurity Threat Assessment Principles
16.10.2. Characteristics of Automated Artificial Intelligence Responses
16.10.3. Critical Factors in the Effectiveness of Cyber Responses with Artificial Intelligence 

Module 17. Intrusion Detection and Prevention Using Generative Artificial Intelligence Models

17.1. Fundamentals of IDS/IPS Systems and the Role of Artificial Intelligence

17.1.1. Definition and Basic Principles of IDS and IPS Systems
17.1.2. Main Types and Configurations of IDS/IPS
17.1.3. Contribution of Artificial Intelligence in the Evolution of Detection and Prevention Systems

17.2. Use of Gemini for Network Anomaly Detection

17.2.1. Concepts and Types of Anomalies in Network Traffic
17.2.2. Gemini's Features for Network Data Analysis
17.2.3. Benefits of Anomaly Detection in Intrusion Prevention

17.3. Gemini and the Identification of Intrusion Patterns

17.3.1. Principles of Intrusion Pattern Identification and Classification
17.3.2. Artificial Intelligence Techniques Applied in the Detection of Threat Patterns
17.3.3. Types of Patterns and Anomalous Behavior in Network Security

17.4. Application of Generative Models in Attack Simulation

17.4.1. Fundamentals of Generative Models in Artificial Intelligence
17.4.2. Use of Generative Models to Recreate Attack Scenarios
17.4.3. Advantages and Limitations of Attack Simulation Using Generative Artificial Intelligence

17.5. Clustering and Event Classification Using Artificial Intelligence

17.5.1. Fundamentals of Clustering and Classification in Intrusion Detection
17.5.2. Common Clustering Algorithms Applied in Cybersecurity
17.5.3. Role of Artificial Intelligence in Improving Event Classification Methods

17.6. Gemini in the Generation of Behavioral Profiles

17.6.1. User and Device Profiling Concepts
17.6.2. Application of Generative Models in the Creation of Profiles
17.6.3. Benefits of Behavioral Profiling in Threat Detection

17.7. Big Data Analysis for Intrusion Prevention

17.7.1. Importance of Big Data in Detecting Security Patterns
17.7.2. Methods for Processing Large Volumes of Data in Cybersecurity
17.7.3. Artificial Intelligence Applications in Analysis and Prevention Based on Big Data

17.8. Data Reduction and Selection of Relevant Features with Artificial Intelligence

17.8.1. Principles of Dimensionality Reduction in Large Data Volumes
17.8.2. Feature Selection to Improve the Efficiency of Artificial Intelligence Analysis 
17.8.3. Data Reduction Techniques Applied in Cybersecurity

17.9. Evaluation of Artificial Intelligence Models in Intrusion Detection

17.9.1. Evaluation Criteria of Artificial Intelligence Models in Cybersecurity
17.9.2. Performance and Accuracy Indicators of the Models
17.9.3. Importance of Constant Validation and Evaluation in Artificial Intelligence

17.10. Implementation of an Intrusion Detection System Powered by Generative Artificial Intelligence

17.10.1. Basic Concepts of Intrusion Detection System Implementation
17.10.2. Integration of Generative Artificial Intelligence in IDS/IPS Systems
17.10.3. Key Aspects for the Configuration and Maintenance of Artificial Intelligence-Based Systems

Module 18. Modern Cryptography with ChatGPT Support for Data Protection

18.1. Basic Principles of Cryptography with Artificial Intelligence Applications

18.1.1. Fundamental Concepts of Cryptography: Confidentiality and Authenticity
18.1.2. Main Cryptographic Algorithms and Their Current Relevance
18.1.3. Role of Artificial Intelligence in the Modernization of Cryptography

18.2. ChatGPT in the Teaching and Practice of Symmetric and Asymmetric Cryptography

18.2.1. Introduction to Symmetric and Asymmetric Cryptography
18.2.2. Comparison between Symmetric and Asymmetric Encryption
18.2.3. Use of ChatGPT in Learning Cryptographic Methods

18.3. Advanced Encryption (AES, RSA) and AI-Generated Recommendations

18.3.1. Fundamentals of AES and RSA Algorithms in Data Encryption
18.3.2. Strengths and Weaknesses of These Algorithms in the Current Context
18.3.3. Generation of Security Recommendations in Advanced Cryptography with Artificial Intelligence

18.4. Artificial Intelligence in Key Management and Authentication

18.4.1. Principles of Cryptographic Key Management
18.4.2. Importance of Secure Key Authentication
18.4.3. Application of Artificial Intelligence to Optimize Key Management and Authentication Processes

18.5. Hashing Algorithms and ChatGPT in Integrity Assessment

18.5.1. Basic Concepts and Applications of Hashing Algorithms
18.5.2. Hashing Functions in Data Integrity Verification
18.5.3. Data Integrity Analysis and Verification with the Help of ChatGPT

18.6. ChatGPT in the Detection of Anomalous Encryption Patterns

18.6.1. Introduction to Anomalous Pattern Detection in Cryptography
18.6.2. ChatGPT's Ability to Identify Irregularities in Cryptographic Data
18.6.3. Limitations of Language Models in Anomalous Cipher Detection

18.7. Introduction to Post-Quantum Cryptography with Artificial Intelligence Simulations

18.7.1. Fundamentals of Post-Quantum Cryptography and Its Importance
18.7.2. Main Post-Quantum Algorithms in Research
18.7.3. Use of Artificial Intelligence in Simulations for the Study of Post-Quantum Cryptography 

18.8. Blockchain and ChatGPT in the Verification of Secure Transactions

18.8.1. Basic Concepts of Blockchain and Its Security Structure
18.8.2. Role of Cryptography in Blockchain Integrity
18.8.3. Application of ChatGPT to Explain and Analyze Secure Transactions

18.9. Privacy Protection and Federated Learning

18.9.1. Definition and Principles of Federated Learning
18.9.2. Importance of Privacy in Decentralized Learning
18.9.3. Benefits and Challenges of Federated Learning for Data Security

18.10. Development of a Generative Artificial Intelligence Based Encryption System

18.10.1. Basic Principles in the Creation of Encryption Systems
18.10.2. Advantages of Generative Artificial Intelligence in the Design of Encryption Systems
18.10.3. Components and Requirements of an AI-Assisted Encryption System

Module 19. Digital Forensics and Artificial Intelligence-Assisted Incident Response

19.1. ChatGPT Forensic Processes for the Identification of Evidence

19.1.1. Basic Concepts of Forensic Analysis in Digital Environments
19.1.2. Stages of Evidence Identification and Collection
19.1.3. Role of ChatGPT in the Support of Forensic Identification

19.2. Gemini and ChatGPT in Data Identification and Data Mining

19.2.1. Fundamentals of Data Extraction for Forensic Analysis
19.2.2. Relevant Data Identification Techniques
19.2.3. Contribution of Artificial Intelligence to the Automation of the Extraction Process

19.3. Log Analysis and Event Correlation with Artificial Intelligence

19.3.1. Importance of Logs in Incident Analysis
19.3.2. Event Correlation Techniques for Incident Reconstruction
19.3.3. Use of Artificial Intelligence to Identify Patterns in Log Correlation

19.4. Data Recovery and Restoration of Systems Using Artificial Intelligence

19.4.1. Data Recovery Principles and Their Importance in Digital Forensics
19.4.2. Restoration Techniques of Compromised Systems
19.4.3. Application of Artificial Intelligence to Improve Recovery and Restoration Processes

19.5. Machine Learning for Incident Detection and Reconstruction

19.5.1. Introduction to Machine Learning in Incident Detection
19.5.2. Incident Reconstruction Techniques with Artificial Intelligence Models
19.5.3. Ethical and Practical Considerations in Event Detection

19.6. Incident Reconstruction and Simulation with ChatGPT

19.6.1. Fundamentals of Incident Reconstruction in Forensic Analysis
19.6.2. ChatGPT's Ability to Create Incident Simulations
19.6.3. Limitations and Challenges in Complex Incident Simulation 

19.7. Detection of Malicious Activity on Mobile Devices

19.7.1. Characteristics and Challenges in Forensic Analysis of Mobile Devices
19.7.2. Major Malicious Activities in Mobile Environments
19.7.3. Application of Artificial Intelligence to Identify Threats in Mobile Devices

19.8. Automated Incident Response with Artificial Intelligence Workflows

19.8.1. Principles of Incident Response in Cybersecurity
19.8.2. Importance of Automation in Rapid Incident Response
19.8.3. Benefits of Artificial Intelligence-Assisted Workflows in Mitigation

19.9. Ethics and Transparency in Forensic Analysis with Generative AI

19.9.1. Ethical Principles in the Use of Artificial Intelligence in Forensic Analysis
19.9.2. Transparency and Explainability of Generative Models in Forensics
19.9.3. Privacy and Accountability Considerations in Analysis

19.10. Forensic Analysis and Incident Recreation Lab with ChatGPT and Gemini

19.10.1. Structure and Objectives of a Forensic Analysis Laboratory
19.10.2. Benefits of Controlled Environments for Forensics Practice
19.10.3. Key Components for Setting Up a Simulation Laboratory 

Module 20. Predictive Models for Proactive Defense in Cybersecurity Using ChatGPT

20.1. Predictive Analytics in Cybersecurity: Techniques and Applications with Artificial Intelligence

20.1.1. Basic Concepts of Predictive Analytics in Security
20.1.2. Predictive Techniques in the Field of Cybersecurity
20.1.3. Application of Artificial Intelligence in the Anticipation of Cyber Threats

20.2. Regression and Classification Models with ChatGPT Support

20.2.1. Principles of Regression and Classification in Threat Prediction
20.2.2. Types of Classification Models in Cybersecurity
20.2.3. ChatGPT Assistance in the Interpretation of Predictive Models

20.3. Identifying Emerging Threats with ChatGPT Predictions

20.3.1. Emerging Threat Detection Concepts
20.3.2. Techniques for Identifying New Attack Patterns
20.3.3. Limitations and Precautions in the Prediction of New Threats

20.4. Neural Networks for Anticipation of Cyberattacks

20.4.1. Fundamentals of Neural Networks Applied in Cybersecurity
20.4.2. Common Architectures for Detection and Prediction of Attacks
20.4.3. Challenges in Implementing Neural Networks in Cyber Defense 

20.5. Use of ChatGPT for Threat Scenario Simulations

20.5.1. Basic Concepts of Threat Simulation in Cybersecurity
20.5.2. ChatGPT Capabilities for Developing Predictive Simulations 
20.5.3. Factors to Consider in the Design of Simulated Scenarios

20.6. Reinforcement Learning Algorithms for Optimization of Defenses

20.6.1. Introduction to Reinforcement Learning in Cybersecurity
20.6.2. Reinforcement Algorithms Applied to Defense Strategies 
20.6.3. Benefits and Challenges of Reinforcement Learning in Cybersecurity Environments

20.7. Threat Simulation and Response with ChatGPT

20.7.1. Threat Simulation Principles and Their Relevance in Cyber Defense
20.7.2. Automated and Optimized Responses to Simulated Attacks
20.7.3. Benefits of Simulation for Improving Cyber Preparedness

20.8. Accuracy and Effectiveness Assessment in Predictive Artificial Intelligence Models

20.8.1. Key Indicators for the Evaluation of Predictive Models
20.8.2. Accuracy Assessment Methodologies in Cybersecurity Models
20.8.3. Critical Factors in the Effectiveness of Artificial Intelligence Models in Cybersecurity 

20.9. Artificial Intelligence in Incident Management and Automated Response

20.9.1. Fundamentals of Incident Management in Cybersecurity
20.9.2. Role of Artificial Intelligence in Real-Time Decision Making 
20.9.3. Challenges and Opportunities in Response Automation

20.10. Creation of a Predictive Defense System with ChatGPT Support

20.10.1. Proactive Defense System Design Principles
20.10.2. Integration of Predictive Models in Cybersecurity Environments
20.10.3. Key Components for an AI-Based Predictive Defense System 

You will delve into the integration of ChatGPT in risk analysis and automated incident response, to manage highly complex digital environments with precision”