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 practical contents with which they are created, provide scientific and practical information on the disciplines that are essential 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 university program will provide specialists with a set of technical skills and specialized knowledge to effectively apply AI in the diagnosis, treatment and management of oral health. The academic pathway will focus on providing a thorough understanding of the fundamentals of AI, as well as its specific application in the interpretation of radiographic images, analysis of clinical data and development of predictive tools for dental conditions.

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle
• Evaluate the crucial role of data in the development and implementation of AI solutions
• Delve into algorithms and complexity to solve specific problems
• Explore the theoretical basis of neural networks for Deep Learning development
• Explore bio-inspired computing and its relevance in the development of intelligent systems
• Analyze current strategies of Artificial Intelligence in various fields, identifying opportunities and challenges
• Gain a solid understanding of Machine Learning principles and their specific application in dental contexts
• Analyze dental data, including visualization techniques to improve diagnostics
• Acquire advanced skills in the application of AI for the accurate diagnosis of oral diseases and interpretation of dental images
• Understand the ethical and privacy considerations associated with the application of AI in dentistry
• Explore ethical challenges, regulations, professional liability, social impact, access to dental care, sustainability, policy development, innovation, and future prospects in the application of AI in dentistry

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence 

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing  their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Explore the concept of the semantic web and its influence on the organization and understanding of information in digital environments

Module 2. Data Types and Data Life Cycle 

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data
• Analyze the life cycle of data, from generation to disposal, identifying key stages
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure
• Study data collection processes, including methodology, tools and collection channels
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design
• Analyze the regulatory aspects related to data management, complying with privacy and security regulations, as well as best practices

Module 3. Data in Artificial Intelligence 

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Analyze supervised and unsupervised models, including methods and classification
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence

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

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set
• Address data preprocessing in Big Data environments

Module 5. Algorithm and Complexity in Artificial Intelligence 

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts
• Explore tree-based algorithms, understanding their structure and applications
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems 

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems
• Study semantic reasoners, knowledge-based systems and expert systems, understanding their functionality and applications in intelligent decision making

Module 7. Machine Learning and Data Mining

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers
• Analyze regression and continuous response models for predicting numerical values from data
• Study clustering techniques to identify patterns and structures in unlabeled data sets
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text

Module 8. Neural networks, the basis of Deep Learning 

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately
• Understanding the effective linking of layers and operations to design complex and efficient neural network architectures
• Use trainers and optimizers to tune and improve the performance of neural networks
• Explore the connection between biological and artificial neurons for a deeper understanding of model design
• Tuning hyperparameters for Fine Tuning of neural networks, optimizing their performance on specific tasks

Module 9. Deep Neural Networks Training 

• Solve gradient-related problems in deep neural network training
• Explore and apply different optimizers to improve the efficiency and convergence of models
• Program the learning rate to dynamically adjust the convergence speed of the model
• Understand and address overfitting through specific strategies during training
• Apply practical guidelines to ensure efficient and effective training of deep neural networks
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization
• Develop practical applications using Transfer Learning to solve real-world problems
• Understand and apply regularization techniques to improve generalization and avoid overfitting in deep neural networks

Module 10. Model Customization and Training with TensorFlow 

• Master the fundamentals of TensorFlowand its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow
• Explore the tfdata API to efficiently manage and manipulate datasets
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow
• Use Keras preprocessing layers to facilitate the construction of custom models
• Explore the TensorFlow Datasets  project to access predefined datasets and improve development efficiency
• Develop a Deep Learning application with TensorFlow, integrating the knowledge acquired in the module
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations

Module 11. Deep Computer Vision with Convolutional Neural Networks 

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision
• Explore and apply convolutional layers to extract key features from images
• Implement clustering layers and their use in  Deep Computer Vision models with Keras
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance
• Use pre-trained Keras models to leverage transfer learning for specific tasks
• Apply classification and localization techniques in Deep Computer Vision environments
• Explore object detection and object tracking strategies using Convolutional Neural Networks
• Implement semantic segmentation techniques to understand and classify objects in images in a detailed manner

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

• Developing skills in text generation using Recurrent Neural Networks (RNN)
• Apply RNNs in opinion classification for sentiment analysis in texts
• Understand and apply attentional mechanisms in natural language processing models
• Analyze and use Transformers models in specific NLP tasks
• Explore the application of Transformers models in the context of image processing and computer vision
• Become familiar with the Hugging Face’s Transformers library for efficient implementation of advanced models
• Compare different Transformers libraries to evaluate their suitability for specific tasks
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems

Module 13. Autoencoders, GANs, and Diffusion Models 

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation
• Implement and understand the operation of stacked autoencoders
• Explore and apply convolutional autoencoders for efficient visual data representations
• Analyze and apply the effectiveness of sparse automatic encoders in data representation
• Generate fashion images from the MNIST dataset using Autoencoders
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models
• Implement and compare the performance of Diffusion Models and GANs in data generation

Module 14. Bio-Inspired Computing  

• Introduce the fundamental concepts of bio-inspired computing
• Explore social adaptation algorithms as a key approach in bio-inspired computing
• Analyze space exploration-exploitation strategies in genetic algorithms
• Examine models of evolutionary computation in the context of optimization 
• Continue detailed analysis of evolutionary computation models 
• Apply evolutionary programming to specific learning problems
• Address the complexity of multi-objective problems in the framework of bio-inspired computing
• Explore the application of neural networks in the field of bio-inspired computing 
• Delve into the implementation and usefulness of neural networks in bio-inspired computing

Module 15. Artificial Intelligence: Strategies and Applications

• Develop strategies for the implementation of artificial intelligence in financial services
• Analyze the implications of artificial intelligence in the delivery of healthcare services
• Identify and assess the risks associated with the use of AI in the healthcare field
• Assess the potential risks associated with the use of AI in industry
• Apply artificial intelligence techniques in industry to improve productivity
• Design artificial intelligence solutions to optimize processes in public administration
• Evaluate the implementation of AI technologies in the education sector
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity
• Optimize human resources processes through the strategic use of artificial intelligence

Module 16. Fundamentals of AI in Dentistry

• Acquire solid knowledge of the basic principles of Machine Learning and its specific application in dental contexts
• Learn methods and tools for analyzing dental data, as well as visualization techniques that enhance interpretation and diagnosis
• Develop a thorough understanding of the ethical and privacy considerations associated with the application of AI in dentistry, promoting responsible practices in the use of these technologies in clinical settings
• Familiarize students with the various applications of AI in the field of dentistry, such as oral disease diagnosis, treatment planning, and patient care management
• Design personalized dental treatment plans according to the specific needs of each patient, taking into account factors such as genetics, medical history and individual preferences

Module 17. AI-assisted Dental Diagnostics and Treatment Planning 

• Acquire expertise in the use of AI for treatment planning, including 3D modeling, orthodontic treatment optimization and treatment plan customization
• Develop advanced skills in the application of AI for the accurate diagnosis of oral diseases, including interpretation of dental images and pathology detection
• Obtain competencies to use AI tools in oral health monitoring and oral disease prevention, effectively integrating these technologies into dental practice
• Collect, manage and use both clinical and radiographic data in AI treatment planning
• Enable students to evaluate and select AI technologies suitable for their dental practice, considering aspects such as accuracy, reliability and scalability

Module 18. Innovations and Practical Applications of AI in Dentistry

• Develop specialized skills in the application of AI in 3D printing, robotics, dental materials development, clinical management, teleodontology, and automation of administrative tasks, addressing diverse areas of dental practice
• Acquire the ability to strategically implement AI in dental education and training, ensuring that practitioners are equipped to adapt to constantly evolving technological innovations in the dental field
• Develop specialized skills in the application of AI in 3D printing, robotics, dental materials development, and automation of administrative tasks
• Employ AI to analyze patient feeback, optimizing clinical management in dental clinics to improve patient experience
• Strategically implement AI in dental education, ensuring that professionals are equipped to adapt to the ever-evolving technological innovations in the dental field

Module 19. Advanced Analytics and Data Processing in Dentistry

• Handle large datasets in dentistry, understanding the concepts and applications of Big Data, as well as the implementation of data mining and predictive analytics techniques
• Acquire expertise in the application of AI in various aspects, such as dental epidemiology, clinical data management, social network analysis and clinical research, using machine learning algorithms
• Develop advanced skills in the management of large data sets in dentistry, understanding the concepts and applications of Big Data, as well as the implementation of data mining and predictive analytics techniques
• Employ AI tools for monitoring oral health trends and patterns, contributing to more efficient management
• Explore and discuss the various ways in which data analytics is used to improve clinical decision making, patient care management and research in Dentistry

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

• Understand and address ethical challenges related to the use of AI in dentistry, promoting responsible professional practices
• Inquire into the regulations and standards relevant to the application of AI in Dentistry, developing skills in policy formulation to ensure safe and ethical practices
• Address the social, educational, business and sustainable impact of AI in dentistry, to adapt to changes in dental practice in the era of advanced AI
• Manage the tools necessary to understand and address the ethical challenges related to the use of AI in Dentistry, promoting responsible professional practices
• Provide students with a thorough understanding of the social, business and sustainable impact of AI in the field of dentistry, preparing them to lead and adapt to changes that arise during their professional practice

You will get up to date with the most current applications in Artificial Intelligence and apply them to your daily clinical practice as a dentist”