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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 Data 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 their 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. Data Warehouse (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/Safety

2.10. Regulatory Aspects

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Normative 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. Merge Sort
5.3.6. 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. Analysis of Results

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
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. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input layer
8.3.2. Cloak
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. Learning Transfer Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
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. Learning Transfer 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. Learning Transfer 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. Grap Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Preprocessing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tf.data API

10.6.1. Using the tf.dataAPI 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 Preprocessing Layers

10.8.1. Using the Keras Preprocessing API
10.8.2. Preprocessing Pipelined Construction with Keras
10.8.3. Using the Keras Preprocessing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets  for Data Loading
10.9.2. Preprocessing Data 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. Use of the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex 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. Architecture ResNet

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
11.10.2. Edge Detection
11.10.3. 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 TransformersModels 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 Preprocessing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s TransformersBookstore

12.8.1. Using the 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. Automatic Encoder Denoising

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 (AI) in Financial Services.  Opportunities and Challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/uses of AI

15.2. Implications of Artificial Intelligence in the Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Uses

15.3. Risks Related to the Use of AI in the Health Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/uses of AI

15.4. Retail

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

15.5. Industry

15.5.1. Implications of AI in Industry. Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential risks related to the use of AI in industry

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/uses of AI

15.7. Public Administration

15.7.1. AI implications for public administration. Opportunities and Challenges
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/uses of AI

15.8. Educational

15.8.1. AI implications for education. Opportunities and Challenges
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/uses of AI

15.9. Forestry and Agriculture

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

15.10 Human Resources

15.10.1. Implications of AI for Human Resources Opportunities and Challenges
15.10.2. Case Uses
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/uses of AI

Module 16. Software Development Productivity Improvement with AI

16.1. Preparing a Suitable Development Environment

16.1.1. Essential Tools selection for AI Development
16.1.2. Configuration of the Selected Tools
16.1.3. Implementation of CI/CD Pipelines Adapted to AI Projects
16.1.4. Efficient Management of Dependencies and Versions in Development Environments

16.2. Essential AI Extensions for Visual Studio Code

16.2.1. Exploring and Selecting AI Extensions for Visual Studio Code
16.2.2. Integrating Static and Dynamic Analysis Tools into the Integrated Development Environment (IDE)
16.2.3. Automation of Repetitive Tasks with Specific Extensions
16.2.4. Customization of the Development Environment to Improve Efficiency

16.3. No-Code Design of User Interfaces with AI Elements

16.3.1. No-Code Design Principles and their Application to User Interfaces
16.3.2. Incorporation of AI Elements in Visual Interface Design
16.3.3. Tools and Platforms for the No-Code Creation of Intelligent Interfaces
16.3.4. Evaluation and Continuous Improvement of No-code Interfaces with AI

16.4. Code Optimization Using ChatGPT

16.4.1. Duplicate Code Detection
16.4.2. Refactor
16.4.3. Create Readable Code
16.4.4. Understanding What Code Does
16.4.5. Improving Variable and Function Naming
16.4.6. Creating Automatic Documentation

16.5. Repository Management with AI

16.5.1. Automation of Version Control Processes with AI Techniques
16.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
16.5.3. Predictive Analysis of Changes and Trends in Code Repositories
16.5.4. Improvements in the Organization and Categorization of Repositories using AI

16.6. Integration of AI in Database Management

16.6.1. Optimization of Queries and Performance Using AI Techniques
16.6.2. Predictive Analysis of Database Access Patterns
16.6.3. Implementation of Recommender Systems to Optimize Database Structure
16.6.4. Proactive Monitoring and Detection of Potential Database Problems

16.7. Fault Detection and Creation of Unit Tests with AI

16.7.1. Automatic Generation of Test Cases using AI Techniques
16.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with AI
16.7.3. Improving Test Coverage by Identifying Critical Areas by AI

16.8. Pair Programming with GitHub Copilot

16.8.1. Integration and Effective Use of GitHub Copilot in Pair Programming Sessions
16.8.2. Integration Improvements in Communication and Collaboration among Developers with GitHub Copilot
16.8.3. Integration Strategies to Maximize the Use of GitHub Copilot-Generated Code suggestions
16.8.4. Integration Case Studies and Best Practices in AI-Assisted Pair Programming

16.9. Automatic Translation between Programming Languages

16.9.1. Specific Machine Translation Tools and Services for Programming Languages
16.9.2. Adaptation of Machine Translation Algorithms to Development Contexts
16.9.3. Improvement of Interoperability between Different Languages by Machine Translation
16.9.4. Assessment and Mitigation of Potential Challenges and Limitations in Machine Translation

16.10. Recommended AI Tools to Improve Productivity

16.10.1. Comparative Analysis of AI Tools for Software Development
16.10.2. Integration of AI Tools in Workflows
16.10.3. Automation of Routine Tasks with AI Tools
16.10.4. Evaluation and Selection of Tools Based on Project Context and Requirements

Module 17. Software Architecture with AI

17.1. Optimization and Performance Management in AI Tools

17.1.1. Performance Analysis and Profiling in AI tools
17.1.2. Algorithm Optimization Strategies and AI Models
17.1.3. Implementation of Caching and Parallelization Techniques to Improve Performance
17.1.4. Tools and Methodologies for Continuous Real-Time Performance Monitoring

17.2. Scalability in AI Applications

17.2.1. calable Architectures Design for AI Applications
17.2.2. Implementation of Partitioning and Load Sharing Techniques
17.2.3. Workflow and Workload Management in Scalable Systems
17.2.4. Strategies for Horizontal and Vertical Expansion in Variable Demand Environments

17.3. Maintainability of AI Applications

17.3.1. Design Principles to Facilitate Maintainability in IA Projects
17.3.2. Specific Documentation Strategies for AI Models and Algorithms
17.3.3. Implementation of Unit and Integration Tests to Facilitate Maintainability
17.3.4. Methods for Refactoring and Continuous Improvement in Systems with AI Components

17.4. Large-Scale System Design

17.4.1. Architectural Principles for Large-Scale System Design
17.4.2. Decomposition of Complex Systems into Microservices
17.4.3. Implementation of Specific Design Patterns for Distributed Systems
17.4.4. Strategies for Complexity Management in Large-Scale Architectures with AI Components

17.5. Large-Scale Data Warehousing for AI Tools

17.5.1. Selection of Scalable Data Storage Technologies
17.5.2. Design of Database Schemas for Efficient Handling of Large Data Volumes
17.5.3. Partitioning and Replication Strategies in Massive Data Storage Environments
17.5.4. Implementation of Data Management Systems to Ensure Integrity and Availability in AI Projects

17.6. Data structures with IA

17.6.1. Adaptation of Classical Data Structures for Use with AI Algorithms
17.6.2. Design and Optimization of Specific Data Structures for Machine Learning Models
17.6.3. Integration of Efficient Data Structures in Data Intensive Systems
17.6.4. Strategies for Real-Time Data Manipulation and Storage in AI Data Structures

17.7. Programming Algorithms for AI Products

17.7.1. Development and Implementation of Application-Specific Algorithms for AI Applications
17.7.2. Algorithm Selection Strategies according to Problem Type and Product Requirements
17.7.3. Adaptation of Classical Algorithms for Integration into AI Systems
17.7.4. Evaluation and Performance Comparison between Different Algorithms in Development Contexts with AI

17.8. Design Patterns for AI Development

17.8.1. Identification and Application of Common Design Patterns in Projects with AI Components
17.8.2. Development of Specific Patterns for the Integration of Models and Algorithms into Existing Systems
17.8.3. Strategies for the Implementation of Patterns to Improve Reusability and Maintainability in AI Projects
17.8.4. Case Studies and Best Practices in the Application of Design Patterns in AI Architectures

17.9. Implementation of Clean Architecture

17.9.1. Fundamental Principles and Concepts of Clean Architecture
17.9.2. Adaptation of Clean Architecture to Projects with AI Components
17.9.3. Implementation of Layers and Dependencies in Systems with Clean Architecture
17.9.4. Benefits and Challenges of Implementing Clean Architecture in Software Development with AI

17.10. Secure Software Development in Web Applications with AI

17.10.1. Principles of Security in the Development of Software with AI Components
17.10.2. Identification and Mitigation of Potential Vulnerabilities in AI Models and Algorithms
17.10.3. Implementation of Secure Development Practices in Web Applications with Artificial Intelligence Functionalities
17.10.4. Strategies for the Protection of Sensitive Data and Prevention of Attacks in AI projects

Module 18. Website Projects with AI

18.1. Working Environment Preparation for Web Development with AI

18.1.1. Configuration of Web Development Environments for Projects with Artificial Intelligence
18.1.2. Selection and Preparation of Essential Tools for Web Development with AI
18.1.3. Integration of Specific Libraries and Frameworks for Web Projects with Artificial Intelligence
18.1.4. Implementation of Best Practices in the Configuration of Collaborative Development Environments

18.2. Workspace Creation for AI Projects

18.2.1. Effective Design and Organization of Workspaces for Web Projects with Artificial Intelligence Components
18.2.2. Use of Project Management and Version Control Tools in the Workspace
18.2.3. Strategies for Efficient Collaboration and Communication in the Development Team
18.2.4. Adaptation of the Workspace to the Specific Needs of AI Web Projects

18.3. Design Patterns in AI Products

18.3.1. Identification and Application of Common Design Patterns in User Interfaces with Artificial Intelligence Elements
18.3.2. Development of Specific Patterns to Improve the User Experience in AI Web Projects
18.3.3. Integration of Design Patterns in the Overall Architecture of Web Projects with Artificial Intelligence
18.3.4. Evaluation and Selection of Appropriate Design Patterns According to the Project's Context

18.4. Frontend Development with AI

18.4.1. Integration of AI Models in the Presentation Layer of Web Projects
18.4.2. Development of Adaptive User Interfaces with Artificial Intelligence Elements
18.4.3. Implementation of Natural Language Processing (NLP) Functionalities in Frontend Development
18.4.4. Strategies for Performance Optimization in Frontend Development with AI

18.5. Database Creation

18.5.1. Selection of Database Technologies for Web Projects with Artificial Intelligence
18.5.2. Design of Database Schemas for Storing and Managing AI-Related Data
18.5.3. Implementation of Efficient Storage Systems for Large Volumes of Data Generated by AI Models
18.5.4. Strategies for Security and Protection of Sensitive Data in AI Web Project Databases

18.6. Back-End Development with AI

18.6.1. Integration of AI Services and Models in the Back-End Business Logic
18.6.2. Development of Specific APIs and Endpoints for Communication between Front-End and AI Components
18.6.3. Implementation of Data Processing and Decision-Making Logic in the Backend with Artificial Intelligence
18.6.4. Strategies for Scalability and Performance in Back-End Development of Web Projects with AI

18.7. Optimization of the Deployment Process of Your Website

18.7.1. Automation of Web Project Build and Deployment Processes with AI
18.7.2. Implementing CI/CD Pipelines Tailored to Web Applications with Artificial Intelligence Components
18.7.3. Strategies for Efficient Release and Upgrade Management in Continuous Deployments
18.7.4. Post-Deployment Monitoring and Analysis for Continuous Process Improvement

18.8. AI in Cloud Computing

18.8.1. Integration of Artificial Intelligence Services in Cloud Computing Platforms
18.8.2. Development of Scalable and Distributed Solutions using Cloud Services with AI Capabilities
18.8.3. Strategies for Efficient Resource and Cost Management in Cloud Environments with AI-enabled Web Applications
18.8.4. Evaluation and Comparison of Cloud Service Providers for AI-enabled Web Projects

18.9. Creation of AI-enabled Project for LAMP Environments

18.9.1. Adaptation of Web Projects Based on the LAMP Stack to Include Artificial Intelligence Components
18.9.2. Integration of AI-specific Libraries and Frameworks in LAMP Environments
18.9.3. Development of AI Functionalities that Complement the Traditional LAMP Architecture
18.9.4. Strategies for Optimization and Maintenance in Web Projects with AI in LAMP Environments

18.10. Creation of AI-enabled Project for MEVN Environments

18.10.1. Integration of MEVN Stack Technologies and Tools with Artificial Intelligence Components
18.10.2. Development of Modern and Scalable Web Applications in MEVN Environments with AI Capabilities
18.10.3. Implementation of Data Processing and Machine Learning functionalities in MEVN Projects
18.10.4. Strategies for Performance and Security Enhancement of AI-enabled Web Applications in MEVN Environments

Module 19. Mobile Applications with AI

19.1. Working Environment Preparation for mobile Development with AI

19.1.1. Configuration of Mobile Development Environments for Projects with Artificial Intelligence
19.1.2. Selection and Preparation of Specific Tools for Mobile Application Development with AI
19.1.3. Integration of AI-Libraries and Frameworks in Mobile Development Environments
19.1.4. Configuration of Emulators and Real Devices for Testing Mobile Applications with AI Components

19.2. Creation of a Workspace with GitHub Copilot

19.2.1. Integration of GitHub Copilot in Mobile Development Environments
19.2.2. Effective Use of GitHub Copilot for Code Generation in AI Projects
19.2.3. Strategies for Developer Collaboration when Using GitHub Copilot in the Workspace
19.2.4. Best Practices and Limitations in the Use of GitHub Copilot in Mobile Application Development with AI

19.3. Firebase Configuration

19.3.1. Initial Configuration of a Firebase Project for Mobile Development
19.3.2. Firebase Integration in Mobile Applications with Artificial Intelligence Functionality
19.3.3. Use of Firebase Services as Database, Authentication, and Notifications in AI projects
19.3.4. Strategies for Real-Time Data and Event Management in Firebase-enabled Mobile Applications

19.4. Concepts of Clean Architecture, DataSources, Repositories

19.4.1. Fundamental Principles of Clean Architecture in Mobile Development with AI
19.4.2. Implementation of DataSources and Repositories Layers in Clean Architectures
19.4.3. Design and Structuring of Components in Mobile Projects with Clean Architecture Approach
19.4.4. Benefits and Challenges of Implementing Clean Architecture in Mobile Applications with AI

19.5. Authentication Screen Creation

19.5.1. Design and Development of User Interfaces for Authentication Screens in Mobile Applications with IA
19.5.2. Integration of Authentication Services with Firebase in the Login Screen
19.5.3. Use of Security and Data Protection Techniques in the Authentication Screen
19.5.4. Personalization and Customization of the User Experience in the Authentication Screen

19.6. Creation of Dashboard and Navigation

19.6.1. Dashboard Design and Development with Artificial Intelligence Elements
19.6.2. Implementation of Efficient Navigation Systems in Mobile Applications with AI
19.6.3. Integration of AI Functionalities in the Dashboard to Improve User Experience

19.7. Creation of Listing Screen

19.7.1. Development of User Interfaces for Listing Screens in AI-enabled Mobile Applications
19.7.2. Integration of Recommendation and Filtering Algorithms into the Listing Screen
19.7.3. Use of Design Patterns for Effective Presentation of Data in the Listing Screen
19.7.4. Strategies for Efficient Loading of Real-Time Data into the Listing Screen

19.8. Details Screen Creation

19.8.1. Design and Development of Detailed User Interfaces for the Presentation of Specific Information
19.8.2. Integration of AI Functionalities to Enrich the Detailed Screen
19.8.3. Implementation of Interactions and Animations in the Detailed Screen
19.8.4. Strategies for Performance Optimization in Loading and Detail Display in AI-enabled Mobile Applications

19.9. Creation of Settings Screen

19.9.1. Development of User Interfaces for Configuration and Settings in AI-enabled Mobile Applications
19.9.2. Integration of Customized Settings Related to Artificial Intelligence Components
19.9.3. Implementation of Customized Options and Preferences in the Settings Screen
19.9.4. Strategies for Usability and Clarity in the Presentation of Options in the Settings Screen

19.10. Creation of Icons, Splash and Graphic Resources for Your App with AI

19.10.1. Design and Creation of Attractive Icons to Represent the AI Mobile Application
19.10.2. Development of Splash Screens with Impactful Visuals
19.10.3. Selection and Adaptation of Graphic Resources to Enhance the Aesthetics of the Mobile Application
19.10.4. Strategies for Consistency and Visual Branding in the Graphic Elements of the Application with AI

Module 20. AI for QA Testing

20.1. Software Testing Life Cycle

20.1.1. Description and Understanding of the Testing Life Cycle in Software Development
20.1.2. Phases of the Testing Life Cycle and its Importance in Quality Assurance
20.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle
20.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using AI

20.2. Test Cases and Bug Detection

20.2.1. Effective Test Case Design and Writing in the Context of QA Testing
20.2.2. Identification of Bugs and Errors during Test Case Execution
20.2.3. Application of Early Bug Detection Techniques using Static Analysis
20.2.4. Use of Artificial Intelligence Tools for the Automatic Identification of Bugs in Test Cases

20.3. Types of Testing

20.3.1. Exploration of Different Types of Testing in the QA Environment
20.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications
20.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in AI Projects
20.3.4. Adaptation of Conventional Testing Types to Projects with Artificial Intelligence Components

20.4. Creation of a Testing Plan

20.4.1. Design and Structure of a Comprehensive Testing Plan
20.4.2. Identification of Requirements and Test Scenarios in AI Projects
20.4.3. Strategies for Manual and Automated Test Planning
20.4.4. Continuous Evaluation and Adjustment of the Testing Plan as the Project Develops

20.5. AI Bug Detection and Reporting

20.5.1. Implementation of Automatic Bug Detection Techniques using Machine Learning Algorithms
20.5.2. Use of Artificial Intelligence Tools for Dynamic Code Analysis to Search for Possible Bugs
20.5.3. Strategies for Automatic Generation of Detailed Reports on AI-Detected Bugs
20.5.4. Effective Collaboration between Development and QA Teams in the Management of AI-Detected Bugs

20.6. Creation of Automated Testing with AI

20.6.1. Development of Automated Test Scripts for Projects with AI Components
20.6.2. Integration of AI-Based Test Automation Tools
20.6.3. Use of Machine Learning Algorithms for Dynamic Generation of Automated Test Cases
20.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in AI Projects

20.7. API Testing

20.7.1. Fundamental Concepts of API Testing and its Importance in QA
20.7.2. Development of Tests for the Verification of APIs in Environments with Artificial Intelligence Components
20.7.3. Strategies for Data and Results Validation in API Testing with AI
20.7.4. Use of Specific Tools for API Testing in Projects with Artificial Intelligence

20.8. AI Tools for Web Testing

20.8.1. Exploration of Artificial Intelligence Tools for Test Automation in Web Environments
20.8.2. Integration of Element Recognition and Visual Analysis Technologies in Web Testing
20.8.3. Strategies for Automatic Detection of Changes and Performance Problems in Web Applications Using AI
20.8.4. Evaluation of Specific Tools for Improving Efficiency in Web Testing with AI

20.9. Mobile Testing Using AI

20.9.1. Development of Testing Strategies for Mobile Applications with AI Components
20.9.2. Integration of Specific Testing Tools for AI-Based Mobile Platforms
20.9.3. Use of Machine Learning Algorithms for Detecting Performance Problems in Mobile Applications
20.9.4. Strategies for the Validation of Interfaces and Specific Functions of Mobile Applications by AI

20.10. QA Tools with AI

20.10.1. Exploration of QA Tools and Platforms that Incorporate Artificial Intelligence Functionality
20.10.2. Evaluation of Tools for Efficient Test Management and Test Execution in AI Projects
20.10.3. Use of Machine Learning Algorithms for Test Case Generation and Optimization
20.10.4. Strategies for Effective Selection and Adoption of QA Tools with AI Capabilitie

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