Through this 100% online Professional master’s degree, you will address the impact of Big Data in Dentistry, examining key concepts and applications”

Bio-inspired Computing is an interdisciplinary field that draws inspiration from nature and biological processes to design algorithms. Its main objective is to address complex problems and find innovative solutions. For example, this tool is useful for solving optimization difficulties in route planning, network design and resource allocation.  Likewise, bio-inspired systems are used in anomaly detection by modeling behavior in complex systems (such as computer networks) to identify threats or attacks. 

In this context, TECH is developing a university program that will delve into Bio-inspired Computing, taking into account social adaptation algorithms. The syllabus will analyze various space exploration-exploitation strategies for genetic algorithms. In turn, the syllabus will examine evolutionary programming applied to learning problems. The program will also offer students emerging technologies to improve their dental practice, including 3-D printing, robotic systems and teleodontology. This will enable graduates to provide high quality services, while differentiating themselves from the rest. 

Moreover, the revolutionary Relearning method is used to ensure gradual learning for students. It is scientifically proven that this teaching model, of which TECH is a pioneer, serves to assimilate knowledge progressively. To this end, it is based on the reiteration of the main concepts so that they remain in the memory without the extra effort involved in memorizing. At the same time, the syllabus is complemented by various audiovisual resources, including explanatory videos, interactive summaries and infographics. All students will need is an electronic device (such as a cell phone, computer or tablet) with Internet access to access the Virtual Campus and expand their knowledge through the most innovative academic content. In addition, the university program includes real case studies in simulated learning environments.

Get a solid foundation in the principles of Artificial Intelligence in Dentistry . Get up to speed with an advanced and adaptable academic program!”  

This Professional master’s degree in Artificial Intelligence in Dentistry 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 in Dentistry
• 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 be able to interpret from dental images through applications of Computational Intelligence, all thanks to the most innovative multimedia resources"  

The program’s teaching staff includes professionals from the sector who contribute their work experience to this training 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 academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

The use of Machine Learning in Dentistry will improve the accuracy of your diagnoses and treatments"

Relearning will allow you to learn with less effort and more performance, getting more involved in your professional specialization"

This program will provide students with a holistic and multidisciplinary vision of the integration of AI in dentistry.  The syllabus will delve into the fundamentals of Machine Learning, data analysis and 3D printing. In this way, students will acquire a profound vision of the technological evolution in the dental field. Likewise, the syllabus will delve into Data Mining, aimed at locating patterns in oral health records to predict the risk of developing diseases. In addition, the balanced approach between theory and practice will enable graduates to lead the responsible adoption of Machine Learning.

A university program that will prepare you to adopt advanced technologies and make a leap in quality in your dental practice"

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-based Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialogue 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. Creation of 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 Indices 
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. Criteria for Mathematical Analysis of 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 Sorting (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

18.1. 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. Layer Bonding 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. 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. 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. Using the TensorFlow library
10.1.2. Model Education with TensorFlow
10.1.3. Operations with graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy computational 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 functions and graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Optimization of graphs with TensorFlow operations

10.5. Data loading and preprocessing with TensorFlow

10.5.1. Loading of datasets with TensorFlow
10.5.2. Data preprocessing with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The API tfdata

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

10.7. The TFRecord format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. Loading TFRecord Files with TensorFlow
10.7.3. Using TFRecord Files for Training Models

10.8. Keras Preprocessing Layers

10.8.1. Using the Keras Preprocessing API
10.8.2. Construction of preprocessing pipelined 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. Data preprocessing with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning application with TensorFlow

10.10.1. Practical Applications
10.10.2. Building a Deep Learning application with TensorFlow
10.10.3. Training a model 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 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 Natural Recurrent Networks (NNN) 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. Use of 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 Preprocessing
12.7.3. Training a TransformersModel for Vision

12.8. Hugging Face Transformer Library

12.8.1. Using the Hugging Face Transformers Library
12.8.2. Application of the Hugging Face Transformers Library
12.8.3. Advantages of the Hugging Face Transformers library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison between the 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 Applications

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
13.4.3Results Evaluation

13.5. Automatic Encoder Denoising

13.5.1. Application of Filters
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. Model Implementation

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 the 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. Monitoring and Control of Dental Health using AI

16.1. AI Applications for Patient's Dental Health Management

16.1.1. Design of Mobile Applications for Dental Hygiene Monitoring
16.1.2. AI Systems for the Early Detection of Caries and Periodontal Diseases
16.1.3. Use of AI in the Personalization of Dental Treatments
16.1.4. Image Recognition Technologies for Automated Dental Diagnostics

16.2. Integration of Clinical and Biomedical Information as a Basis for Dental Health Monitoring

16.2.1. Platforms for Integration of Clinical and Radiographic Data
16.2.2. Analysis of Medical Records to Identify Dental Risks
16.2.3. Systems for Correlating Biomedical Data with Dental Conditions
16.2.4. Tools for the Unified Management of Patient Information

16.3. Definition of Indicators for the Control of the Patient's Dental Health

16.3.1. Establishment of Parameters for the Evaluation of Oral Health
16.3.2. Systems for Monitoring Progress in Dental Treatments
16.3.3. Development of Risk Indexes for Dental Disease
16.3.4. AI Methods for the Prediction of Future Dental Problems

16.4. Natural Language Processing of Dental Health Records for Indicator Extraction

16.4.1. Automatic Extraction of Relevant Data from Dental Records
16.4.2. Analysis of Clinical Notes to Identify Dental Health Trends
16.4.3. Use of NLP to Summarize Long Medical Records
16.4.4. Early Warning Systems Based on Clinical Text Analysis

16.5. AI Tools for the Monitoring and Control of Dental Health Indicators

16.5.1. Development of Applications for Monitoring Oral Hygiene and Oral Health
16.5.2. AI-based Personalized Patient Alerts Systems
16.5.3. Analytical Tools for Continuous Assessment of Dental Health
16.5.4. Use of Wearables and Sensors for Real-Time Dental Monitoring

16.6. Development of Dashboards for the Monitoring of Dental Indicators

16.6.1. Creation of Intuitive Interfaces for Dental Health Monitoring
16.6.2. Integration of Data from Different Clinical Sources into a Single Dashboard
16.6.3. Data Visualization Tools for Treatment Monitoring
16.6.4. Customization of Dashboards According to the Needs of the Dental Professional

16.7. Interpretation of Dental Health Indicators and Decision Making

16.7.1. Data-driven Clinical Decision Support Systems
16.7.2. Predictive Analytics for Dental Treatment Planning
16.7.3. AI for Interpretation of Complex Oral Health Indicators
16.7.4. Tools for the Evaluation of Treatment Effectiveness

16.8. Generation of Dental Health Reports using AI Tools

16.8.1. Automation of the Creation of Detailed Dental Reports
16.8.2. Customized Report Generation Systems for Patients
16.8.3. AI Tools for Summarizing Clinical Findings
16.8.4. Integration of Clinical and Radiological Data into Automated Reports

16.9. AI-enabled Platforms for Patient Monitoring of Dental Health

16.9.1. Applications for Oral Health Self-monitoring
16.9.2. AI-based Interactive Dental Education Platforms
16.9.3. Tools for Symptom Tracking and Personalized Dental Advice
16.9.4. Gamification Systems to Encourage Good Dental Hygiene Habits

16.10. Security and Privacy in the Treatment of Dental Information

16.10.1. Security Protocols for the Protection of Patient Data
16.10.2. Encryption and Anonymization Systems in the Management of Clinical Data
16.10.3. Regulations and Legal Compliance in the Management of Dental Information
16.10.4. Privacy Education and Awareness for Professionals and Patients 

Module 17. AI-assisted Dental Diagnostics and Treatment Planning

17.1. AI in Oral Disease Diagnosis

17.1.1. Use of Machine Learning Algorithms to Identify Oral Diseases
17.1.2. Integration of AI in Diagnostic Equipment for Real-Time Analysis
17.1.3. AI-assisted Diagnostic Systems to Improve Accuracy
17.1.4. Analysis of Symptoms and Clinical Signals through AI for Rapid Diagnostics

17.2. Dental Image Analysis with AI

17.2.1. Development of Software for the Automatic Interpretation of Dental Radiographs
17.2.2. AI in the Detection of Abnormalities in Oral MRI Images
17.2.3. Improvement in the Quality of Dental Imaging through AI Technologies
17.2.4. Deep Learning Algorithms for Classifying Dental Conditions in Imaging

17.3. AI in Caries and Dental Pathology Detection

17.3.1. Pattern Recognition Systems for Identifying Early Cavities
17.3.2. AI for Risk Assessment of Dental Pathologies
17.3.3. Computer Vision Technologies in the Detection of Periodontal Diseases
17.3.4. AI Tools for Caries Monitoring and Progression

17.4. 3D Modeling and Treatment Planning with AI

17.4.1. Using AI to Create Accurate 3D Models of the Oral Cavity
17.4.2. AI Systems in the Planning of Complex Dental Surgeries
17.4.3. Simulation Tools for Predicting Treatment Outcomes
17.4.4. AI in the Customization of Prosthetics and Dental Appliances

17.5. Optimization of Orthodontic Treatments using AI

17.5.1. AI in the Planning and Follow-up of Orthodontic Treatments
17.5.2. Algorithms for the Prediction of Tooth Movements and Orthodontic Adjustments
17.5.3. AI Analysis to Reduce Orthodontic Treatment Time
17.5.4. Real-time Remote Monitoring and Treatment Adjustment Systems

17.6. Risk Prediction in Dental Treatments

17.6.1. AI Tools for Risk Assessment in Dental Procedures
17.6.2. Decision Support Systems for Identifying Potential Complications
17.6.3. Predictive Models for Anticipating Treatment Reactions
17.6.4. Analysis of Clinical Histories using AI to Personalize Treatments

17.7. Personalization of Treatment Plans with AI

17.7.1. AI in the Adaptation of Dental Treatments to Individual Needs
17.7.2. AI-based Treatment Recommender Systems
17.7.3. Analysis of Oral Health Data for Personalized Treatment Planning
17.7.4. AI Tools for Adjusting Treatments Based on Patient Response

17.8. Oral Health Monitoring with Intelligent Technologies

17.8.1. Smart Devices for Oral Hygiene Monitoring
17.8.2. AI-enabled Mobile Applications for Dental Health Monitoring
17.8.3. Wearables with Sensors to Detect Changes in Oral Health
17.8.4. AI-based Early Warning Systems to Prevent Oral Diseases

17.9. AI in Oral Disease Prevention

17.9.1. AI Algorithms to Identify Risk Factors for Oral Diseases
17.9.2. Oral Health Education and Awareness Systems with AI
17.9.3. Predictive Tools for the Early Prevention of Dental Problems
17.9.4. AI in the Promotion of Healthy Habits for Oral Prevention

17.10. Case Studies: Diagnostic and Planning Successes with AI

17.10.1. Analysis of Real Cases where AI Improved Dental Diagnosis
17.10.2. Successful Case Studies on the Implementation of AI for Treatment Planning
17.10.3. Treatment Comparisons with and without the Use of AI
17.10.4. Documentation of Improvements in Clinical Efficiency and Effectiveness with AI 

Module 18. Innovation with AI in Dentistry

18.1. 3D Printing and Digital Fabrication in Dentistry

18.1.1. Use of 3D Printing for the Creation of Customized Dental Prostheses
18.1.2. Fabrication of Orthodontic Splints and Aligners using 3D Technology
18.1.3. Development of Dental Implants using 3D Printing
18.1.4. Application of Digital Fabrication Techniques in Dental Restoration

18.2. Robotics in Dental Procedures

18.2.1. Implementation of Robotic Arms for Precision Dental Surgeries
18.2.2. Use of Robots in Endodontic and Periodontic Procedures
18.2.3. Development of Robotic Systems for Dental Operations Assistance
18.2.4. Integration of Robotics in the Practical Teaching of Dentistry

18.3. Development of AI-assisted Dental Materials

18.3.1. Use of AI to Innovate in Dental Restorative Materials
18.3.2. Predictive Analytics for Durability and Efficiency of New Dental Materials
18.3.3. AI in the Optimization of Properties of Materials such as Resins and Ceramics
18.3.4. AI Systems to Customize Materials according to Patient's Needs

18.4. AI-enabled Dental Practice Management

18.4.1. AI Systems for Efficient Appointment and Scheduling Management
18.4.2. Data Analysis to Improve Quality of Dental Services
18.4.3. AI Tools for Inventory Management in Dental Clinics
18.4.4. Use of AI in the Evaluation and Continuous Improvement of Dental Practice

18.5. Teleodontology and Virtual Consultations

18.5.1. Tele-dentistry Platforms for Remote Consultations
18.5.2. Use of Videoconferencing Technologies for Remote Diagnosis
18.5.3. AI Systems for Online Preliminary Assessment of Dental Conditions
18.5.4. Tools for Secure Communication between Patients and Dentists

18.6. Automation of Administrative Tasks in Dental Clinics

18.6.1. Implementation of AI Systems for Billing and Accounting Automation
18.6.2. Use of AI Software in Patient Record Management
18.6.3. AI Tools for Optimization of Administrative Workflows
18.6.4. Automatic Scheduling and Reminder Systems for Dental Appointments

18.7. Sentiment Analysis of Patient Opinions

18.7.1. Use of AI to Assess Patient Satisfaction through Online Feedback
18.7.2. Natural Language Processing Tools for Analyzing Patient Feedback
18.7.3. AI Systems to Identify Areas for Improvement in Dental Services
18.7.4. Analysis of Patient Trends and Perceptions using AI

18.8. AI in Marketing and Patient Relationship Management

18.8.1. Implementation of AI Systems to Personalize Dental Marketing Strategies
18.8.2. AI Tools for Customer Behavioral Analysis
18.8.3. Use of AI in the Management of Marketing Campaigns and Promotions
18.8.4. AI-based Patient Recommendation and Loyalty Systems

18.9. Safety and Maintenance of AI Dental Equipment

18.9.1. AI Systems for Monitoring and Predictive Maintenance of Dental Equipment
18.9.2. Use of AI in Ensuring Compliance with Safety Regulations
18.9.3. Automated Diagnostic Tools for Equipment Failure Detection
18.9.4. Implementation of AI-assisted Safety Protocols in Dental Practices

18.10. Integration of AI in Dental Education and Training

18.10.1. Use of AI in Simulators for Hands-on Training in Dentistry
18.10.2. AI Tools for the Personalization of Learning in Dentistry
18.10.3. Systems for Evaluation and Monitoring of Educational Progress using AI
18.10.4. Integration of AI Technologies in the Development of Curricula and Didactic Materials 

Module 19. Advanced Analytics and Data Processing in Dentistry  

19.1. Big Data in Dentistry: Concepts and Applications 

19.1.1. The Explosion of Data in Dentistry
19.1.2. Concept of Big Data
19.1.3. Applications of Big Data in Dentistry

19.2. Data Mining in Dental Records 

19.2.1. Main Methodologies for Data Mining
19.2.2. Integration of Data from Dental Records
19.2.3. Detection of Patterns and Anomalies in Dental Records

19.3. Advanced Predictive Analytics Techniques in Oral Health 

19.3.1. Classification Techniques for Oral Health Analysis
19.3.2. Regression Techniques for Oral Health Analytics
19.3.3. Deep Learning for Oral Health Analysis

19.4. AI Models for Dental Epidemiology  

19.4.1. Classification Techniques for Dental Epidemiology
19.4.2. Regression Techniques for Dental Epidemiology
19.4.3. Unsupervised Techniques for Dental Epidemiology

19.5. AI for Clinical and Radiographic Data Management  

19.5.1. Integration of Clinical Data for Effective Management with AI Tools
19.5.2. Transformation of Radiographic Diagnosis using Advanced AI Systems
19.5.3. Integrated Management of Clinical and Radiographic Data

19.6. Machine Learning Algorithms in Dental Research  

19.6.1. Classification Techniques in Dental Research
19.6.2. Regression Techniques in Dental Research 
19.6.3. Unsupervised Techniques in Dental Research

19.7. Social Network Analysis in Oral Health Communities  

19.7.1. Introduction to Social Network Analysis
19.7.2. Analysis of Opinions and Sentiment in Social Networks in Oral Health Communities 
19.7.3. Analysis of Social Network Trends in Oral Health Communities 

19.8. AI in Monitoring Oral Health Trends and Patterns  

19.8.1. Early Detection of Epidemiologic Trends with AI
19.8.2. Continuous Monitoring of Oral Hygiene Patterns with AI Systems
19.8.3. Prediction of Changes in Oral Health with AI Models

19.9. AI Tools for Cost Analysis in Dentistry  

19.9.1. Optimization of Resources and Costs with AI Tools
19.9.2. Efficiency and Cost-Effectiveness Analysis in Dental Practices with AI
19.9.3. Cost Reduction Strategies Based on AI-analyzed Data

19.10. Innovations in AI for Dental Clinical Research  

19.10.1. Implementation of Emerging Technologies in Dental Clinical Research
19.10.2. Improving the Validation of Dental Clinical Research Results with AI
19.10.3. Multidisciplinary Collaboration in AI-powered Detailed Clinical Research

Module 20. Ethics, Regulation and the Future of AI in Dentistry  

20.1. Ethical Challenges in the Use of AI in Dentistry  

20.1.1. Ethics in AI-assisted Clinical Decision Making
20.1.2. Patient Privacy in Intelligent Dentistry Environments
20.1.3. Professional Accountability and Transparency in AI Systems

20.2. Ethical Considerations in the Collection and Use of Dental Data

20.2.1. Informed Consent and Ethical Data Management in Dentistry
20.2.2. Security and Confidentiality in the Handling of Sensitive Data
20.2.3. Ethics in Research with Large Datasets in Dentistry

20.3. Fairness and Bias in AI Algorithms in Dentistry

20.3.1. Addressing Bias in Algorithms to Ensure Fairness
20.3.2. Ethics in the Implementation of Predictive Algorithms in Oral Health
20.3.3. Ongoing Monitoring to Mitigate Bias and Promote Equity

20.4. Regulations and Standards in Dental AI  

20.4.1. Regulatory Compliance in the Development and Use of AI Technologies
20.4.2. Adaptation to Legal Changes in the Deployment of IA Systems
20.4.3. Collaboration with Regulatory Authorities to Ensure Compliance

20.5. AI and Professional Responsibility in Dentistry  

20.5.1. Development of Ethical Standards for Professionals using AI
20.5.2. Professional Responsibility in the Interpretation of AI Results
20.5.3. Continuing Education in Ethics for Oral Health Professionals

20.6. Social Impact of AI in Dental Care

20.6.1. Social Impact Assessment for Responsible Introduction of AI
20.6.2. Effective Communication about AI Technologies with Patients
20.6.3. Community Participation in the Development of Dental Technologies

20.7. AI and Access to Dental Care  

20.7.1. Improving Access to Dental Services through AI Technologies
20.7.2. Addressing Accessibility Challenges with AI Solutions
20.7.3. Equity in the Distribution of AI-assisted Dental Services

20.8. AI and Sustainability in Dental Practices  

20.8.1. Energy Efficiency and Waste Reduction with AI Implementation
20.8.2. Sustainable Practice Strategies Enhanced by AI Technologies
20.8.3. Environmental Impact Assessment in the Integration of AI Systems

20.9. AI Policy Development for the Dental Sector  

20.9.1. Collaboration with Institutions for the Development of Ethical Policies
20.9.2. Creation of Best Practice Guidelines on the Use of AI
20.9.3. Active Participation in the Formulation of AI-related Government Policies

20.10. Ethical Risk and Benefit Assessment of AI in Dentistry

20.10.1. Ethical Risk Analysis in the Implementation of AI Technologies
20.10.2. Ongoing Assessment of Ethical Impact on Dental Care
20.10.3. Long-term Benefits and Risk Mitigation in the Deployment of AI Systems

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences"